The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Torino, Italia
May, 2024

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Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

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Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Nicoletta Calzolari | Min-Yen Kan | Veronique Hoste | Alessandro Lenci | Sakriani Sakti | Nianwen Xue

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3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset
Xinyu Ma | Xuebo Liu | Derek F. Wong | Jun Rao | Bei Li | Liang Ding | Lidia S. Chao | Dacheng Tao | Min Zhang

Multimodal machine translation (MMT) is a challenging task that seeks to improve translation quality by incorporating visual information. However, recent studies have indicated that the visual information provided by existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities. This issue presents a significant obstacle to the development of MMT research. This paper presents a novel solution to this issue by introducing 3AM, an ambiguity-aware MMT dataset comprising 26,000 parallel sentence pairs in English and Chinese, each with corresponding images. Our dataset is specifically designed to include more ambiguity and a greater variety of both captions and images than other MMT datasets. We utilize a word sense disambiguation model to select ambiguous data from vision-and-language datasets, resulting in a more challenging dataset. We further benchmark several state-of-the-art MMT models on our proposed dataset. Experimental results show that MMT models trained on our dataset exhibit a greater ability to exploit visual information than those trained on other MMT datasets. Our work provides a valuable resource for researchers in the field of multimodal learning and encourages further exploration in this area. The data, code and scripts are freely available at https://github.com/MaxyLee/3AM.

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A Benchmark Evaluation of Clinical Named Entity Recognition in French
Nesrine Bannour | Christophe Servan | Aurélie Névéol | Xavier Tannier

Background: Transformer-based language models have shown strong performance on many Natural Language Processing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adapted to different languages and sub-domains through training or fine-tuning on specific corpora while remaining lighter than modern Large Language Models (MLMs). Recently, several MLMs have been released for the biomedical domain in French, and experiments suggest that they outperform standard French counterparts. However, no systematic evaluation comparing all models on the same corpora is available. Objective: This paper presents an evaluation of masked language models for biomedical French on the task of clinical named entity recognition. Material and methods: We evaluate biomedical models CamemBERT-bio and DrBERT and compare them to standard French models CamemBERT, FlauBERT and FrAlBERT as well as multilingual mBERT using three publically available corpora for clinical named entity recognition in French. The evaluation set-up relies on gold-standard corpora as released by the corpus developers. Results: Results suggest that CamemBERT-bio outperforms DrBERT consistently while FlauBERT offers competitive performance and FrAlBERT achieves the lowest carbon footprint. Conclusion: This is the first benchmark evaluation of biomedical masked language models for French clinical entity recognition that compares model performance consistently on nested entity recognition using metrics covering performance and environmental impact.

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A Benchmark for Recipe Understanding in Artificial Agents
Jens Nevens | Robin de Haes | Rachel Ringe | Mihai Pomarlan | Robert Porzel | Katrien Beuls | Paul van Eecke

This paper introduces a novel benchmark that has been designed as a test bed for evaluating whether artificial agents are able to understand how to perform everyday activities, with a focus on the cooking domain. Understanding how to cook recipes is a highly challenging endeavour due to the underspecified and grounded nature of recipe texts, combined with the fact that recipe execution is a knowledge-intensive and precise activity. The benchmark comprises a corpus of recipes, a procedural semantic representation language of cooking actions, qualitative and quantitative kitchen simulators, and a standardised evaluation procedure. Concretely, the benchmark task consists in mapping a recipe formulated in natural language to a set of cooking actions that is precise enough to be executed in the simulated kitchen and yields the desired dish. To overcome the challenges inherent to recipe execution, this mapping process needs to incorporate reasoning over the recipe text, the state of the simulated kitchen environment, common-sense knowledge, knowledge of the cooking domain, and the action space of a virtual or robotic chef. This benchmark thereby addresses the growing interest in human-centric systems that combine natural language processing and situated reasoning to perform everyday activities.

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ABLE: Agency-BeLiefs Embedding to Address Stereotypical Bias through Awareness Instead of Obliviousness
Michelle YoungJin Kim | Junghwan Kim | Kristen Johnson

Natural Language Processing (NLP) models tend to inherit and amplify stereotypical biases present in their training data, leading to harmful societal consequences. Current efforts to rectify these biases typically revolve around making models oblivious to bias, which is at odds with the idea that humans require increased awareness to tackle these biases better. This prompts a fundamental research question: are bias-oblivious models the only viable solution to combat stereotypical biases? This paper answers this question by proposing the Agency-BeLiefs Embedding (ABLE) model, a novel approach that actively encodes stereotypical biases into the embedding space. ABLE draws upon social psychological theory to acquire and represent stereotypical biases in the form of agency and belief scores rather than directly representing stereotyped groups. Our experimental results showcase ABLE’s effectiveness in learning agency and belief stereotypes while preserving the language model’s proficiency. Furthermore, we underscore the practical significance of incorporating stereotypes within the ABLE model by demonstrating its utility in various downstream tasks. Our approach exemplifies the potential benefits of addressing bias through awareness, as opposed to the prevailing approach of mitigating bias through obliviousness.

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Abstractive Multi-Video Captioning: Benchmark Dataset Construction and Extensive Evaluation
Rikito Takahashi | Hirokazu Kiyomaru | Chenhui Chu | Sadao Kurohashi

This paper introduces a new task, abstractive multi-video captioning, which focuses on abstracting multiple videos with natural language. Unlike conventional video captioning tasks generating a specific caption for a video, our task generates an abstract caption of the shared content in a video group containing multiple videos. To address our task, models must learn to understand each video in detail and have strong abstraction abilities to find commonalities among videos. We construct a benchmark dataset for abstractive multi-video captioning named AbstrActs. AbstrActs contains 13.5k video groups and corresponding abstract captions. As abstractive multi-video captioning models, we explore two approaches: end-to-end and cascade. For evaluation, we proposed a new metric, CocoA, which can evaluate the model performance based on the abstractness of the generated captions. In experiments, we report the impact of the way of combining multiple video features, the overall model architecture, and the number of input videos.

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Abstract-level Deductive Reasoning for Pre-trained Language Models
Xin Wu | Yi Cai | Ho-fung Leung

Pre-trained Language Models have been shown to be able to emulate deductive reasoning in natural language. However, PLMs are easily affected by irrelevant information (e.g., entity) in instance-level proofs when learning deductive reasoning. To address this limitation, we propose an Abstract-level Deductive Reasoner (ADR). ADR is trained to predict the abstract reasoning proof of each sample, which guides PLMs to learn general reasoning patterns rather than instance-level knowledge. Experimental results demonstrate that ADR significantly reduces the impact of PLMs learning instance-level knowledge (over 70%).

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A Call for Clarity in Beam Search: How It Works and When It Stops
Jungo Kasai | Keisuke Sakaguchi | Ronan Le Bras | Dragomir Radev | Yejin Choi | Noah A. Smith

Text generation with beam search has proven successful in a wide range of applications. We point out that, though largely overlooked in the literature, the commonly-used implementation of beam decoding (e.g., Hugging Face Transformers and fairseq) uses a first come, first served heuristic: it keeps a set of already completed sequences over time steps and stops when the size of this set reaches the beam size. Based on this finding, we introduce a patience factor, a simple modification to this beam decoding implementation, that generalizes the stopping criterion and provides flexibility to the depth of search. Empirical results demonstrate that adjusting this patience factor improves decoding performance of strong pretrained models on news text summarization and machine translation over diverse language pairs, with a negligible inference slowdown. Our approach only modifies one line of code and can be thus readily incorporated in any implementation. Further, we find that different versions of beam decoding result in large performance differences in summarization, demonstrating the need for clarity in specifying the beam search implementation in research work. Our code will be available upon publication.

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A Canonical Form for Flexible Multiword Expressions
Jan Odijk | Martin Kroon

This paper proposes a canonical form for Multiword Expressions (MWEs), in particular for the Dutch language. The canonical form can be enriched with all kinds of annotations that can be used to describe the properties of the MWE and its components. It also introduces the DUCAME (DUtch CAnonical Multiword Expressions) lexical resource with more than 11k MWEs in canonical form. DUCAME is used in MWE-Finder to automatically generate queries for searching for flexible MWEs in large text corpora.

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A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation
Jifan Yu | Xiaohan Zhang | Yifan Xu | Xuanyu Lei | Zijun Yao | Jing Zhang | Lei Hou | Juanzi Li

Empowered by the large-scale pretrained language models, existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations. However, they are still plagued by the <b>hallucination</b> problem, causing unpredictable factual errors in the generated responses. Recently, knowledge-grounded dialogue generation models, that intentionally invoke external knowledge resources to more informative responses, are also proven to be effective in reducing hallucination. Following the idea of getting high-quality knowledge, a few efforts have achieved pretty good performance on this issue. As some inevitable knowledge noises may also lead to hallucinations, it is emergent to investigate the reason and future directions for building noise-tolerant methods in KGD tasks. In this paper, we analyze the causal story behind this problem with counterfactual reasoning methods. Based on the causal effect analysis, we propose a possible solution for alleviating the hallucination in KGD by exploiting the dialogue-knowledge interaction. Experimental results of our example implementation show that this method can reduce hallucination without disrupting other dialogue performance, while keeping adaptive to different generation models. We hope our efforts can support and call for more attention to developing lightweight techniques towards robust and trusty dialogue systems.

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Access Control Framework for Language Collections
Ben Foley | Peter Sefton | Simon Musgrave | Moises Sacal Bonequi

This paper introduces the licence-based access control framework developed by the Language Data Commons of Australia (LDaCA) for a range of language collections, with examples given of implementation for significant Indigenous and Australian English collections. Language collections may be curated for many reasons, such as documentation for language revival, for research, security or commercial purposes. Some language collections are created with the intention of being “Open Access”; publicly available with no restriction. Other collections require that access be limited to individuals or groups of people, either at the collection level or at the level of individual items, such as a recording. To facilitate access, while respecting the intended access conditions for a collection, or collection items, some form of user identification and authorisation process is typically required. The access control framework described in this paper is based upon descriptions of access conditions in easy-to-read licences which are stored alongside data files in the collections; and is implemented using identity-based authentication and authorisation systems where required. The framework accommodates accessibility needs from unrestricted to extremely limited access, is dynamic, and able to be modified in response to changes in access needs. Storing licences with the data is a significant development in separating language data and access requirements from access infrastructure.

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A Challenge Dataset and Effective Models for Conversational Stance Detection
Fuqiang Niu | Min Yang | Ang Li | Baoquan Zhang | Xiaojiang Peng | Bowen Zhang

Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discussions concerning the same specific topic, as naturally transpire in authentic social media interactions. This constraint arises primarily due to the scarcity of datasets that authentically replicate real social media contexts, hindering the research progress of conversational stance detection. In this paper, we introduce a new multi-turn conversation stance detection dataset (called MT-CSD), which encompasses multiple targets for conversational stance detection. To derive stances from this challenging dataset, we propose a global-local attention network (GLAN) to address both long and short-range dependencies inherent in conversational data. Notably, even state-of-the-art stance detection methods, exemplified by GLAN, exhibit an accuracy of only 50.47%, highlighting the persistent challenges in conversational stance detection. Furthermore, our MT-CSD dataset serves as a valuable resource to catalyze advancements in cross-domain stance detection, where a classifier is adapted from a different yet related target. We believe that MT-CSD will contribute to advancing real-world applications of stance detection research. Our source code, data, and models are available at https://github.com/nfq729/MT-CSD.

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A Closer Look at Clustering Bilingual Comparable Corpora
Anna Laskina | Eric Gaussier | Gaelle Calvary

We study in this paper the problem of clustering comparable corpora, building upon the observation that different types of clusters can be present in such corpora: monolingual clusters comprising documents in a single language, and bilingual or multilingual clusters comprising documents written in different languages. Based on a state-of-the-art deep variant of Kmeans, we propose new clustering models fully adapted to comparable corpora and illustrate their behavior on several bilingual collections (in English, French, German and Russian) created from Wikipedia.

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AcnEmpathize: A Dataset for Understanding Empathy in Dermatology Conversations
Gyeongeun Lee | Natalie Parde

Empathy is critical for effective communication and mental health support, and in many online health communities people anonymously engage in conversations to seek and provide empathetic support. The ability to automatically recognize and detect empathy contributes to the understanding of human emotions expressed in text, therefore advancing natural language understanding across various domains. Existing empathy and mental health-related corpora focus on broader contexts and lack domain specificity, but similarly to other tasks (e.g., learning distinct patterns associated with COVID-19 versus skin allergies in clinical notes), observing empathy within different domains is crucial to providing tailored support. To address this need, we introduce AcnEmpathize, a dataset that captures empathy expressed in acne-related discussions from forum posts focused on its emotional and psychological effects. We find that transformer-based models trained on our dataset demonstrate excellent performance at empathy classification. Our dataset is publicly released to facilitate analysis of domain-specific empathy in online conversations and advance research in this challenging and intriguing domain.

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A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances
Nigel Ward | Divette Marco

Automatic measures of similarity between sentences or utterances are invaluable for training speech synthesizers, evaluating machine translation, and assessing learner productions. While there exist measures for semantic similarity and prosodic similarity, there are as yet none for pragmatic similarity. To enable the training of such measures, we developed the first collection of human judgments of pragmatic similarity between utterance pairs. 9 judges listened to 220 utterance pairs, each consisting of an utterance extracted from a recorded dialog and a re-enactment of that utterance under various conditions designed to create various degrees of similarity. Each pair was rated on a continuous scale. The average inter-judge correlation was 0.45. We make this data available at https://github.com/divettemarco/PragSim .

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A Community-Driven Data-to-Text Platform for Football Match Summaries
Pedro Fernandes | Sérgio Nunes | Luís Santos

Data-to-text systems offer a transformative approach to generating textual content in data-rich environments. This paper describes the architecture and deployment of Prosebot, a community-driven data-to-text platform tailored for generating textual summaries of football matches derived from match statistics. The system enhances the visibility of lower-tier matches, traditionally accessible only through data tables. Prosebot uses a template-based Natural Language Generation (NLG) module to generate initial drafts, which are subsequently refined by the reading community. Comprehensive evaluations, encompassing both human-mediated and automated assessments, were conducted to assess the system’s efficacy. Analysis of the community-edited texts reveals that significant segments of the initial automated drafts are retained, suggesting their high quality and acceptance by the collaborators. Preliminary surveys conducted among platform users highlight a predominantly positive reception within the community.

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A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking the Privacy-Utility Trade-off
Stephen Meisenbacher | Nihildev Nandakumar | Alexandra Klymenko | Florian Matthes

The application of Differential Privacy to Natural Language Processing techniques has emerged in relevance in recent years, with an increasing number of studies published in established NLP outlets. In particular, the adaptation of Differential Privacy for use in NLP tasks has first focused on the *word-level*, where calibrated noise is added to word embedding vectors to achieve “noisy” representations. To this end, several implementations have appeared in the literature, each presenting an alternative method of achieving word-level Differential Privacy. Although each of these includes its own evaluation, no comparative analysis has been performed to investigate the performance of such methods relative to each other. In this work, we conduct such an analysis, comparing seven different algorithms on two NLP tasks with varying hyperparameters, including the *epsilon* parameter, or privacy budget. In addition, we provide an in-depth analysis of the results with a focus on the privacy-utility trade-off, as well as open-source our implementation code for further reproduction. As a result of our analysis, we give insight into the benefits and challenges of word-level Differential Privacy, and accordingly, we suggest concrete steps forward for the research field.

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A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation
Yachao Zhao | Bo Wang | Yan Wang | Dongming Zhao | Xiaojia Jin | Jijun Zhang | Ruifang He | Yuexian Hou

While extensive work has examined the explicit and implicit biases in large language models (LLMs), little research explores the relation between these two types of biases. This paper presents a comparative study of the explicit and implicit biases in LLMs grounded in social psychology. Social psychology distinguishes between explicit and implicit biases by whether the bias can be self-recognized by individuals. Aligning with this conceptualization, we propose a self-evaluation-based two-stage measurement of explicit and implicit biases within LLMs. First, the LLM is prompted to automatically fill templates with social targets to measure implicit bias toward these targets, where the bias is less likely to be self-recognized by the LLM. Then, the LLM is prompted to self-evaluate the templates filled by itself to measure explicit bias toward the same targets, where the bias is more likely to be self-recognized by the LLM. Experiments conducted on state-of-the-art LLMs reveal human-like inconsistency between explicit and implicit occupational gender biases. This work bridges a critical gap where prior studies concentrate solely on either explicit or implicit bias. We advocate that future work highlight the relation between explicit and implicit biases in LLMs.

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A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media
Jaya Caporusso | Damar Hoogland | Mojca Brglez | Boshko Koloski | Matthew Purver | Senja Pollak

Dehumanisation involves the perception and/or treatment of a social group’s members as less than human. This phenomenon is rarely addressed with computational linguistic techniques. We adapt a recently proposed approach for English, making it easier to transfer to other languages and to evaluate, introducing a new sentiment resource, the use of zero-shot cross-lingual valence and arousal detection, and a new method for statistical significance testing. We then apply it to study attitudes to migration expressed in Slovene newspapers, to examine changes in the Slovene discourse on migration between the 2015-16 migration crisis following the war in Syria and the 2022-23 period following the war in Ukraine. We find that while this discourse became more negative and more intense over time, it is less dehumanising when specifically addressing Ukrainian migrants compared to others.

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A Computational Approach to Quantifying Grammaticization of English Deverbal Prepositions
Ryo Nagata | Yoshifumi Kawasaki | Naoki Otani | Hiroya Takamura

This paper explores grammaticization of deverbal prepositions by a computational approach based on corpus data. Deverbal prepositions are words or phrases that are derived from a verb and that behave as a preposition such as “regarding” and “according to”. Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions. This paper augments them by methods for measuring the degree of grammaticization of deverbal prepositions based on non-contextualized or contextualized word vectors. Experiments show that the methods correlate well with human judgements (as high as 0.69 in Spearman’s rank correlation coefficient). Using the best-performing method, this paper further shows that the methods support previous findings in linguistics including (i) Deverbal prepositions are marginal in terms of prepositionality; and (ii) The process where verbs are grammaticized into prepositions is gradual. As a pilot study, it also conducts a diachronic analysis of grammaticization of deverbal preposition.

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A Computational Model of Latvian Morphology
Peteris Paikens | Lauma Pretkalniņa | Laura Rituma

In this paper we describe a computational model of Latvian morphology that provides a formal structure for Latvian word form inflection and has been implemented in software for generation, analysis and lemmatization of Latvian word forms. The work was motivated by the need for a NLP inflection model that can cover all the complexity of Latvian language and explicitly enumerate and handle the many exceptions to the general Latvian inflection principles. This is an evolution of earlier work, extending the initial proof of concept model to properly cover Latvian language. We provide a set of morphological paradigms that differ from current linguistic tradition, a set of systematic stem changes and combine it with an extensive lexicon that includes paradigm information and structured morphological attributes for 118 000 lexemes. This model has been applied on both dictionary and corpora data, demonstrating that it provides a good coverage for modern Latvian literary language. We also consider that there is a good potential to extend this also to the related Latgalian language.

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A Concept Based Approach for Translation of Medical Dialogues into Pictographs
Johanna Gerlach | Pierrette Bouillon | Jonathan Mutal | Hervé Spechbach

Pictographs have been found to improve patient comprehension of medical information or instructions. However, tools to produce pictograph representations from natural language are still scarce. In this contribution we describe a system that automatically translates French speech into pictographs to enable diagnostic interviews in emergency settings, thereby providing a tool to overcome the language barrier or provide support in Augmentative and Alternative Communication (AAC) contexts. Our approach is based on a semantic gloss that serves as pivot between spontaneous language and pictographs, with medical concepts represented using the UMLS ontology. In this study we evaluate different available pre-trained models fine-tuned on artificial data to translate French into this semantic gloss. On unseen data collected in real settings, consisting of questions and instructions by physicians, the best model achieves an F0.5 score of 86.7. A complementary human evaluation of the semantic glosses differing from the reference shows that 71% of these would be usable to transmit the intended meaning. Finally, a human evaluation of the pictograph sequences derived from the gloss reveals very few additions, omissions or order issues (<3%), suggesting that the gloss as designed is well suited as a pivot for translation into pictographs.

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A Construction Grammar Corpus of Varying Schematicity: A Dataset for the Evaluation of Abstractions in Language Models
Claire Bonial | Harish Tayyar Madabushi

Large Language Models (LLMs) have been developed without a theoretical framework, yet we posit that evaluating and improving LLMs will benefit from the development of theoretical frameworks that enable comparison of the structures of human language and the model of language built up by LLMs through the processing of text. In service of this goal, we develop the Construction Grammar Schematicity (“CoGS”) corpus of 10 distinct English constructions, where the constructions vary with respect to schematicity, or in other words the level to which constructional slots require specific, fixed lexical items, or can be filled with a variety of elements that fulfill a particular semantic role of the slot. Our corpus constructions are carefully curated to range from substantive, frozen constructions (e.g., Let-alone) to entirely schematic constructions (e.g., Resultative). The corpus was collected to allow us to probe LLMs for constructional information at varying levels of abstraction. We present our own probing experiments using this corpus, which clearly demonstrate that even the largest LLMs are limited to more substantive constructions and do not exhibit recognition of the similarity of purely schematic constructions. We publicly release our dataset, prompts, and associated model responses.

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A Controlled Reevaluation of Coreference Resolution Models
Ian Porada | Xiyuan Zou | Jackie Chi Kit Cheung

All state-of-the-art coreference resolution (CR) models involve finetuning a pretrained language model. Whether the superior performance of one CR model over another is due to the choice of language model or other factors, such as the task-specific architecture, is difficult or impossible to determine due to lack of a standardized experimental setup. To resolve this ambiguity, we systematically evaluate five CR models and control for certain design decisions including the pretrained language model used by each. When controlling for language model size, encoder-based CR models outperform more recent decoder-based models in terms of both accuracy and inference speed. Surprisingly, among encoder-based CR models, more recent models are not always more accurate, and the oldest CR model that we test generalizes the best to out-of-domain textual genres. We conclude that controlling for the choice of language model reduces most, but not all, of the increase in F1 score reported in the past five years.

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A Corpus and Method for Chinese Named Entity Recognition in Manufacturing
Ruiting Li | Peiyan Wang | Libang Wang | Danqingxin Yang | Dongfeng Cai

Manufacturing specifications are documents entailing different techniques, processes, and components involved in manufacturing. There is a growing demand for named entity recognition (NER) resources and techniques for manufacturing-specific named entities, with the development of smart manufacturing. In this paper, we introduce a corpus of Chinese manufacturing specifications, named MS-NERC, including 4,424 sentences and 16,383 entities. We also propose an entity recognizer named Trainable State Transducer (TST), which is initialized with a finite state transducer describing the morphological patterns of entities. It can directly recognize entities based on prior morphological knowledge without training. Experimental results show that TST achieves an overall 82.05% F1 score for morphological-specific entities in zero-shot. TST can be improved through training, the result of which outperforms neural methods in few-shot and rich-resource. We believe that our corpus and model will be valuable resources for NER research not only in manufacturing but also in other low-resource domains.

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A Corpus for Sentence-Level Subjectivity Detection on English News Articles
Francesco Antici | Federico Ruggeri | Andrea Galassi | Katerina Korre | Arianna Muti | Alessandra Bardi | Alice Fedotova | Alberto Barrón-Cedeño

We develop novel annotation guidelines for sentence-level subjectivity detection, which are not limited to language-specific cues. We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way for subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation. We evaluate state-of-the-art multilingual transformer-based models on the task in mono-, multi-, and cross-language settings. For this purpose, we re-annotate an existing Italian corpus. We observe that models trained in the multilingual setting achieve the best performance on the task.

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A Corpus of German Abstract Meaning Representation (DeAMR)
Christoph Otto | Jonas Groschwitz | Alexander Koller | Xiulin Yang | Lucia Donatelli

We present the first comprehensive set of guidelines for German Abstract Meaning Representation (Deutsche AMR, DeAMR) along with an annotated corpus of 400 DeAMR. Taking English AMR (EnAMR) as our starting point, we propose significant adaptations to faithfully represent the structure and semantics of German, focusing particularly on verb frames, compound words, and modality. We validate our annotation through inter-annotator agreement and further evaluate our corpus with a comparison of structural divergences between EnAMR and DeAMR on parallel sentences, replicating previous work that finds both cases of cross-lingual structural alignment and cases of meaningful linguistic divergence. Finally, we fine-tune state-of-the-art multi-lingual and cross-lingual AMR parsers on our corpus and find that, while our small corpus is insufficient to produce quality output, there is a need to continue develop and evaluate against gold non-English AMR data.

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A Corpus of Spontaneous L2 English Speech for Real-situation Speaking Assessment
Sylvain Coulange | Marie-Hélène Fries | Monica Masperi | Solange Rossato

When assessing second language proficiency (L2), evaluation of spontaneous speech performance is crucial. This paper presents a corpus of spontaneous L2 English speech, focusing on the speech performance of B1 and B2 proficiency speakers. Two hundred and sixty university students were recorded during a speaking task as part of a French national certificate in English. This task entailed a 10-minute role-play among 2 or 3 candidates, arguing about a controversial topic, in order to reach a negotiated compromise. Each student’s performance was evaluated by two experts, categorizing them into B2, B1 or below B1 speaking proficiency levels. Automatic diarization, transcription, and alignment at the word level were performed on the recorded conversations, in order to analyse lexical stress realisation in polysyllabic plain words of B1 and B2 proficiency students. Results showed that only 35.4% of the 6,350 targeted words had stress detected on the expected syllable, revealing a common stress shift to the final syllable. Besides a substantial inter-speaker variability (0% to 68.4%), B2 speakers demonstrated a slightly higher stress accuracy (36%) compared to B1 speakers (29.6%). Those with accurate stress placement utilized F0 and intensity to make syllable prominence, while speakers with lower accuracy tended to lengthen words on their last syllables, with minimal changes in other dimensions.

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Action and Reaction Go Hand in Hand! a Multi-modal Dialogue Act Aided Sarcasm Identification
Mohit Singh Tomar | Tulika Saha | Abhisek Tiwari | Sriparna Saha

Sarcasm primarily involves saying something but “meaning the opposite” or “meaning something completely different” in order to convey a particular tone or mood. In both the above cases, the “meaning” is reflected by the communicative intention of the speaker, known as dialogue acts. In this paper, we seek to investigate a novel phenomenon of analyzing sarcasm in the context of dialogue acts with the hypothesis that the latter helps to understand the former better. Toward this aim, we extend the multi-modal MUStARD dataset to enclose dialogue acts for each dialogue. To demonstrate the utility of our hypothesis, we develop a dialogue act-aided multi-modal transformer network for sarcasm identification (MM-SARDAC), leveraging interrelation between these tasks. In addition, we introduce an order-infused, multi-modal infusion mechanism into our proposed model, which allows for a more intuitive combined modality representation by selectively focusing on relevant modalities in an ordered manner. Extensive empirical results indicate that dialogue act-aided sarcasm identification achieved better performance compared to performing sarcasm identification alone. The dataset and code are available at https://github.com/mohit2b/MM-SARDAC.

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Action-Concentrated Embedding Framework: This Is Your Captain Sign-tokening
Hyunwook Yu | Suhyeon Shin | Junku Heo | Hyuntaek Shin | Hyosu Kim | Mucheol Kim

Sign language is the primary communication medium for people who are deaf or have hearing loss. However, given the divergent range of sensory abilities of these individuals, there is a communication gap that needs to be addressed. In this paper, we present action-concentrated embedding (ACE), which is a novel sign token embedding framework. Additionally, to provide a more structured foundation for sign language analysis, we introduce a dedicated notation system tailored for sign language that endeavors to encapsulate the nuanced gestures and movements that are integral with sign communication. The proposed ACE approach tracks a signer’s actions based on human posture estimation. Tokenizing these actions and capturing the token embedding using a short-time Fourier transform encapsulates the time-based behavioral changes. Hence, ACE offers input embedding to translate sign language into natural language sentences. When tested against a disaster sign language dataset using automated machine translation measures, ACE notably surpasses prior research in terms of translation capabilities, improving the performance by up to 5.79% for BLEU-4 and 5.46% for ROUGE-L metric.

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Active Learning Design Choices for NER with Transformers
Robert Vacareanu | Enrique Noriega-Atala | Gus Hahn-Powell | Marco A. Valenzuela-Escarcega | Mihai Surdeanu

We explore multiple important choices that have not been analyzed in conjunction regarding active learning for token classification using transformer networks. These choices are: (i) how to select what to annotate, (ii) decide whether to annotate entire sentences or smaller sentence fragments, (iii) how to train with incomplete annotations at token-level, and (iv) how to select the initial seed dataset. We explore whether annotating at sub-sentence level can translate to an improved downstream performance by considering two different sub-sentence annotation strategies: (i) entity-level, and (ii) token-level. These approaches result in some sentences being only partially annotated. To address this issue, we introduce and evaluate multiple strategies to deal with partially-annotated sentences during the training process. We show that annotating at the sub-sentence level achieves comparable or better performance than sentence-level annotations with a smaller number of annotated tokens. We then explore the extent to which the performance gap remains once accounting for the annotation time and found that both annotation schemes perform similarly.

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A CURATEd CATalog: Rethinking the Extraction of Pretraining Corpora for Mid-Resourced Languages
Jorge Palomar-Giner | Jose Javier Saiz | Ferran Espuña | Mario Mina | Severino Da Dalt | Joan Llop | Malte Ostendorff | Pedro Ortiz Suarez | Georg Rehm | Aitor Gonzalez-Agirre | Marta Villegas

We present and describe two language resources in this paper: CATalog 1.0, the largest text corpus in Catalan to date, and CURATE (Corpus Utility for RAting TExt), a modular, parallelizable pipeline used for processing and scoring documents based on text quality that we have optimised to run in High Performance Cluster (HPC) environments. In the coming sections we describe our data preprocessing pipeline at length; traditional pipelines usually implement a set of binary filters such that a given document is either in or out. In our experience with Catalan, in lower-resource settings it is more practical to instead assign a document a soft score to allow for more flexible decision-making. We describe how the document score is calculated and highlight its interpretability by showing that it is significantly correlated with human judgements as obtained from a comparative judgement experiment. We additionally describe the different subcorpora that make up CATalog 1.0.

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AdaKron: An Adapter-based Parameter Efficient Model Tuning with Kronecker Product
Marco Braga | Alessandro Raganato | Gabriella Pasi

The fine-tuning paradigm has been widely adopted to train neural models tailored for specific tasks. However, the recent upsurge of Large Language Models (LLMs), characterized by billions of parameters, has introduced profound computational challenges to the fine-tuning process. This has fueled intensive research on Parameter-Efficient Fine-Tuning (PEFT) techniques, usually involving the training of a selective subset of the original model parameters. One of the most used approaches is Adapters, which add trainable lightweight layers to the existing pretrained weights. Within this context, we propose AdaKron, an Adapter-based fine-tuning with the Kronecker product. In particular, we leverage the Kronecker product to combine the output of two small networks, resulting in a final vector whose dimension is the product of the dimensions of the individual outputs, allowing us to train only 0.55% of the model’s original parameters. We evaluate AdaKron performing a series of experiments on the General Language Understanding Evaluation (GLUE) benchmark, achieving results in the same ballpark as recent state-of-the-art PEFT methods, despite training fewer parameters.

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Adaptive Reinforcement Tuning Language Models as Hard Data Generators for Sentence Representation
Bo Xu | Yifei Wu | Shouang Wei | Ming Du | Hongya Wang

Sentence representation learning is a fundamental task in NLP. Existing methods use contrastive learning (CL) to learn effective sentence representations, which benefit from high-quality contrastive data but require extensive human annotation. Large language models (LLMs) like ChatGPT and GPT4 can automatically generate such data. However, this alternative strategy also encounters challenges: 1) obtaining high-quality generated data from small-parameter LLMs is difficult, and 2) inefficient utilization of the generated data. To address these challenges, we propose a novel adaptive reinforcement tuning (ART) framework. Specifically, to address the first challenge, we introduce a reinforcement learning approach for fine-tuning small-parameter LLMs, enabling the generation of high-quality hard contrastive data without human feedback. To address the second challenge, we propose an adaptive iterative framework to guide the small-parameter LLMs to generate progressively harder samples through multiple iterations, thereby maximizing the utility of generated data. Experiments conducted on seven semantic text similarity tasks demonstrate that the sentence representation models trained using the synthetic data generated by our proposed method achieve state-of-the-art performance. Our code is available at https://github.com/WuNein/AdaptCL.

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Adaptive Simultaneous Sign Language Translation with Confident Translation Length Estimation
Tong Sun | Biao Fu | Cong Hu | Liang Zhang | Ruiquan Zhang | Xiaodong Shi | Jinsong Su | Yidong Chen

Traditional non-simultaneous Sign Language Translation (SLT) methods, while effective for pre-recorded videos, face challenges in real-time scenarios due to inherent inference delays. The emerging field of simultaneous SLT aims to address this issue by progressively translating incrementally received sign video. However, the sole existing work in simultaneous SLT adopts a fixed gloss-based policy, which suffer from limitations in boundary prediction and contextual comprehension. In this paper, we delve deeper into this area and propose an adaptive policy for simultaneous SLT. Our approach introduces the concept of “confident translation length”, denoting maximum accurate translation achievable from current input. An estimator measures this length for streaming sign video, enabling the model to make informed decisions on whether to wait for more input or proceed with translation. To train the estimator, we construct a training data of confident translation length based on the longest common prefix between translations of partial and complete inputs. Furthermore, we incorporate adaptive training, utilizing pseudo prefix pairs, to refine the offline translation model for optimal performance in simultaneous scenarios. Experimental results on PHOENIX2014T and CSL-Daily demonstrate the superiority of our adaptive policy over existing methods, particularly excelling in situations requiring extremely low latency.

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A Dataset for Named Entity Recognition and Entity Linking in Chinese Historical Newspapers
Baptiste Blouin | Cécile Armand | Christian Henriot

In this study, we present a novel historical Chinese dataset for named entity recognition, entity linking, coreference and entity relations. We use data from Chinese newspapers from 1872 to 1949 and multilingual bibliographic resources from the same period. The period and the language are the main strength of the present work, offering a resource which covers different styles and language uses, as well as the largest historical Chinese NER dataset with manual annotations from this transitional period. After detailing the selection and annotation process, we present the very first results that can be obtained from this dataset. Texts and annotations are freely downloadable from the GitHub repository.

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A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
Lisa Raithel | Hui-Syuan Yeh | Shuntaro Yada | Cyril Grouin | Thomas Lavergne | Aurélie Névéol | Patrick Paroubek | Philippe Thomas | Tomohiro Nishiyama | Sebastian Möller | Eiji Aramaki | Yuji Matsumoto | Roland Roller | Pierre Zweigenbaum

User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts concerning ADRs gathered from diverse sources, including patient fora, social media, and clinical reports in German, French, and Japanese. Our corpus contains annotations covering 12 entity types, four attribute types, and 13 relation types. It contributes to the development of real-world multilingual language models for healthcare. We provide statistics to highlight certain challenges associated with the corpus and conduct preliminary experiments resulting in strong baselines for extracting entities and relations between these entities, both within and across languages.

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Adding SPICE to Life: Speaker Profiling in Multiparty Conversations
Shivani Kumar | Rishabh Gupta | Md. Shad Akhtar | Tanmoy Chakraborty

In the realm of conversational dynamics, individual idiosyncrasies challenge the suitability of a one-size-fits-all approach for dialogue agent responses. Prior studies often assumed the speaker’s persona’s immediate availability, a premise not universally applicable. To address this gap, we explore the Speaker Profiling in Conversations (SPC) task, aiming to synthesize persona attributes for each dialogue participant. SPC comprises three core subtasks: persona discovery, persona-type identification, and persona-value extraction. The first subtask identifies persona-related utterances, the second classifies specific attributes, and the third extracts precise values for the persona. To confront this multifaceted challenge, we’ve diligently compiled SPICE, an annotated dataset, underpinning our thorough evaluation of diverse baseline models. Additionally, we benchmark these findings against our innovative neural model, SPOT, presenting an exhaustive analysis encompassing a nuanced assessment of quantitative and qualitative merits and limitations.

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ADEA: An Argumentative Dialogue Dataset on Ethical Issues Concerning Future A.I. Applications
Christian Hauptmann | Adrian Krenzer | Antonia Krause | Frank Puppe

Introducing ADEA: a German dataset that captures online dialogues and focuses on ethical issues related to future AI applications. This dataset, which includes over 2800 labeled user utterances on four different topics, is specifically designed for the training of chatbots that can navigate the complexities of real-world ethical AI conversations. The creation of these dialogues is the result of two carefully conducted studies in which university students interacted with an argumentative dialogue system. A fundamental part of our methodology is the use of German argument graphs. These graphs not only form the knowledge base of the dialogue system but also serve as an effective annotation scheme for the dialogues. Apart from the introduction of the dataset and the argument graphs, we provide a preliminary benchmark using GPT-4 via the OpenAI API. This provides researchers with a concrete reference point while demonstrating the potential of our dataset. We make our dataset and argument graphs available at https://github.com/HaupChris/ADEA-Dialogue-Dataset.

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A Decade of Scholarly Research on Open Knowledge Graphs
Houcemeddine Turki | Abraham Toluwase Owodunni | Mohamed Ali Hadj Taieb | René Fabrice Bile | Mohamed Ben Aouicha

The proliferation of open knowledge graphs has led to a surge in scholarly research on the topic over the past decade. This paper presents a bibliometric analysis of the scholarly literature on open knowledge graphs published between 2013 and 2023. The study aims to identify the trends, patterns, and impact of research in this field, as well as the key topics and research questions that have emerged. The work uses bibliometric techniques to analyze a sample of 4445 scholarly articles retrieved from Scopus. The findings reveal an ever-increasing number of publications on open knowledge graphs published every year, particularly in developed countries (+50 per year). These outputs are published in highly-referred scholarly journals and conferences. The study identifies three main research themes: (1) knowledge graph construction and enrichment, (2) evaluation and reuse, and (3) fusion of knowledge graphs into NLP systems. Within these themes, the study identifies specific tasks that have received considerable attention, including entity linking, knowledge graph embedding, and graph neural networks.

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A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference
Mokanarangan Thayaparan | Marco Valentino | André Freitas

Integer Linear Programming (ILP) has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI). However, traditional ILP frameworks are non-differentiable, posing critical challenges for the integration of continuous language representations based on deep learning. In this paper, we introduce a novel approach, named Diff-Comb Explainer, a neuro-symbolic architecture for explanation-based NLI based on Differentiable BlackBox Combinatorial Solvers (DBCS). Differently from existing neuro-symbolic solvers, Diff-Comb Explainer does not necessitate a continuous relaxation of the semantic constraints, enabling a direct, more precise, and efficient incorporation of neural representations into the ILP formulation. Our experiments demonstrate that Diff-Comb Explainer achieves superior performance when compared to conventional ILP solvers, neuro-symbolic black-box solvers, and Transformer-based encoders. Moreover, a deeper analysis reveals that Diff-Comb Explainer can significantly improve the precision, consistency, and faithfulness of the constructed explanations, opening new opportunities for research on neuro-symbolic architectures for explainable and transparent NLI in complex domains.

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A Document-Level Text Simplification Dataset for Japanese
Yoshinari Nagai | Teruaki Oka | Mamoru Komachi

Document-level text simplification, a task that combines single-document summarization and intra-sentence simplification, has garnered significant attention. However, studies have primarily focused on languages such as English and German, leaving Japanese and similar languages underexplored because of a scarcity of linguistic resources. In this study, we devised JADOS, the first Japanese document-level text simplification dataset based on newspaper articles and Wikipedia. Our dataset focuses on simplification, to enhance readability by reducing the number of sentences and tokens in a document. We conducted investigations using our dataset. Firstly, we analyzed the characteristics of Japanese simplification by comparing it across different domains and with English counterparts. Moreover, we experimentally evaluated the performances of text summarization methods, transformer-based text simplification models, and large language models. In terms of D-SARI scores, the transformer-based models performed best across all domains. Finally, we manually evaluated several model outputs and target articles, demonstrating the need for document-level text simplification models in Japanese.

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A Dual-View Approach to Classifying Radiology Reports by Co-Training
Yutong Han | Yan Yuan | Lili Mou

Radiology report analysis provides valuable information that can aid with public health initiatives, and has been attracting increasing attention from the research community. In this work, we present a novel insight that the structure of a radiology report (namely, the Findings and Impression sections) offers different views of a radiology scan. Based on this intuition, we further propose a co-training approach, where two machine learning models are built upon the Findings and Impression sections, respectively, and use each other’s information to boost performance with massive unlabeled data in a semi-supervised manner. We conducted experiments in a public health surveillance study, and results show that our co-training approach is able to improve performance using the dual views and surpass competing supervised and semi-supervised methods.

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Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms
Wonkee Lee | Seong-Hwan Heo | Jong-Hyeok Lee

Semi-supervised learning that leverages synthetic data for training has been widely adopted for developing automatic post-editing (APE) models due to the lack of training data. With this aim, we focus on data-synthesis methods to create high-quality synthetic data. Given that APE takes as input a machine-translation result that might include errors, we present a data-synthesis method by which the resulting synthetic data mimic the translation errors found in actual data. We introduce a noising-based data-synthesis method by adapting the masked language model approach, generating a noisy text from a clean text by infilling masked tokens with erroneous tokens. Moreover, we propose selective corpus interleaving that combines two separate synthetic datasets by taking only the advantageous samples to enhance the quality of the synthetic data further. Experimental results show that using the synthetic data created by our approach results in significantly better APE performance than other synthetic data created by existing methods.

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Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark
Feng Jiang | Weihao Liu | Xiaomin Chu | Peifeng Li | Qiaoming Zhu | Haizhou Li

Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings, unveiling the discourse topic structure of a document. Compared with sentence-level topic structure, the paragraph-level topic structure can quickly grasp and understand the overall context of the document from a higher level, benefitting many downstream tasks such as summarization, discourse parsing, and information retrieval. However, the lack of large-scale, high-quality Chinese paragraph-level topic structure corpora restrained relative research and applications. To fill this gap, we build the Chinese paragraph-level topic representation, corpus, and benchmark in this paper. Firstly, we propose a hierarchical paragraph-level topic structure representation with three layers to guide the corpus construction. Then, we employ a two-stage man-machine collaborative annotation method to construct the largest Chinese Paragraph-level Topic Structure corpus (CPTS), achieving high quality. We also build several strong baselines, including ChatGPT, to validate the computability of CPTS on two fundamental tasks (topic segmentation and outline generation) and preliminarily verified its usefulness for the downstream task (discourse parsing).

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A Family of Pretrained Transformer Language Models for Russian
Dmitry Zmitrovich | Aleksandr Abramov | Andrey Kalmykov | Vitaly Kadulin | Maria Tikhonova | Ekaterina Taktasheva | Danil Astafurov | Mark Baushenko | Artem Snegirev | Tatiana Shavrina | Sergei S. Markov | Vladislav Mikhailov | Alena Fenogenova

Transformer language models (LMs) are fundamental to NLP research methodologies and applications in various languages. However, developing such models specifically for the Russian language has received little attention. This paper introduces a collection of 13 Russian Transformer LMs, which spans encoder (ruBERT, ruRoBERTa, ruELECTRA), decoder (ruGPT-3), and encoder-decoder (ruT5, FRED-T5) architectures. We provide a report on the model architecture design and pretraining, and the results of evaluating their generalization abilities on Russian language understanding and generation datasets and benchmarks. By pretraining and releasing these specialized Transformer LMs, we aim to broaden the scope of the NLP research directions and enable the development of industrial solutions for the Russian language.

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A Fast and High-quality Text-to-Speech Method with Compressed Auxiliary Corpus and Limited Target Speaker Corpus
Ye Tao | Chaofeng Lu | Meng Liu | Kai Xu | Tianyu Liu | Yunlong Tian | Yongjie Du

With an auxiliary corpus (non-target speaker corpus) for model pre-training, Text-to-Speech (TTS) methods can generate high-quality speech with a limited target speaker corpus. However, this approach comes with expensive training costs. To overcome the challenge, a high-quality TTS method is proposed, significantly reducing training costs while maintaining the naturalness of synthesized speech. In this paper, we propose an auxiliary corpus compression algorithm that reduces the training cost while the naturalness of the synthesized speech is not significantly degraded. We then use the compressed corpus to pre-train the proposed TTS model CMDTTS, which fuses phoneme and word multi-level prosody modeling components and denoises the generated mel-spectrograms using denoising diffusion probabilistic models (DDPMs). In addition, a fine-tuning step that the conditional generative adversarial network (cGAN) is introduced to embed the target speaker feature and improve speech quality using the target speaker corpus. Experiments are conducted on Chinese and English single speaker’s corpora, and the results show that the method effectively balances the model training speed and the synthesized speech quality and outperforms the current models.

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A Frustratingly Simple Decoding Method for Neural Text Generation
Haoran Yang | Deng Cai | Huayang Li | Wei Bi | Wai Lam | Shuming Shi

We introduce a frustratingly simple, highly efficient, and surprisingly effective decoding method, termed Frustratingly Simple Decoding (FSD), for neural text generation. The idea behind FSD is straightforward: We construct an anti-language model (anti-LM) based on previously generated text, which is employed to penalize the future generation of repetitive content. The anti-LM can be implemented as simple as an n-gram language model or a vectorized variant. In this way, FSD incurs no additional model parameters and negligible computational overhead (FSD can be as fast as greedy search). Despite its simplicity, FSD is surprisingly effective and generalizes across different datasets, models, and languages. Extensive experiments show that FSD outperforms established strong baselines in terms of generation quality, decoding speed, and universality.

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A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions
Shun Inadumi | Seiya Kawano | Akishige Yuguchi | Yasutomo Kawanishi | Koichiro Yoshino

Situated conversations, which refer to visual information as visual question answering (VQA), often contain ambiguities caused by reliance on directive information. This problem is exacerbated because some languages, such as Japanese, often omit subjective or objective terms. Such ambiguities in questions are often clarified by the contexts in conversational situations, such as joint attention with a user or user gaze information. In this study, we propose the Gaze-grounded VQA dataset (GazeVQA) that clarifies ambiguous questions using gaze information by focusing on a clarification process complemented by gaze information. We also propose a method that utilizes gaze target estimation results to improve the accuracy of GazeVQA tasks. Our experimental results showed that the proposed method improved the performance in some cases of a VQA system on GazeVQA and identified some typical problems of GazeVQA tasks that need to be improved.

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Agenda-Driven Question Generation: A Case Study in the Courtroom Domain
Yi Fung | Anoop Kumar | Aram Galstyan | Heng Ji | Prem Natarajan

This paper introduces a novel problem of automated question generation for courtroom examinations, CourtQG. While question generation has been studied in domains such as educational testing and product description, CourtQG poses several unique challenges owing to its non-cooperative and agenda-driven nature. Specifically, not only the generated questions need to be relevant to the case and underlying context, they also have to achieve certain objectives such as challenging the opponent’s arguments and/or revealing potential inconsistencies in their answers. We propose to leverage large language models (LLM) for CourtQG by fine-tuning them on two auxiliary tasks, agenda explanation (i.e., uncovering the underlying intents) and question type prediction. We additionally propose cold-start generation of questions from background documents without relying on examination history. We construct a dataset to evaluate our proposed method and show that it generates better questions according to standard metrics when compared to several baselines.

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A Generative Model for Lambek Categorial Sequents
Jinman Zhao | Gerald Penn

In this work, we introduce a generative model, PLC+, for generating Lambek Categorial Grammar(LCG) sequents. We also introduce a simple method to numerically estimate the model’s parameters from an annotated corpus. Then we compare our model with probabilistic context-free grammars (PCFGs) and show that PLC+ simultaneously assigns a higher probability to a common corpus, and has greater coverage.

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Agent-based Modeling of Language Change in a Small-world Network
Dalmo Buzato | Evandro Cunha

Language change has been the subject of numerous studies in linguistics. However, due to the dynamic and complex nature of this phenomenon, and to the difficulty of obtaining extensive real data of language in use, some of its aspects remain obscure. In recent years, nonetheless, research has used computational modeling to simulate features related to variation, change, propagation, and evolution of languages in speech communities, finding compelling results. In this article, agent-based modeling and simulation is used to study language change. Drawing on previous studies, a speech community was modeled using Zachary’s karate club network, a well-established small-world network model in the field of complex systems. Idiolects were assigned through numerical values for each agent. The results demonstrate that the centrality of each agent in the network, interpreted as social prestige, appears to be a factor influencing change. Additionally, the nature of idiolects also seems to impact the spread of linguistic variants in the language change process. These findings complement the theoretical understanding of the language change phenomenon with new simulation data and provide new avenues for research.

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Agettivu, Aggitivu o Aghjettivu? POS Tagging Corsican Dialects
Alice Millour | Lorenza Brasile | Alberto Ghia | Laurent Kevers

In this paper we present a series of experiments towards POS tagging Corsican, a less-resourced language spoken in Corsica and linguistically related to Italian. The first contribution is Corsican-POS, the first gold standard POS-tagged corpus for Corsica, composed of 500 sentences manually annotated with the Universal POS tagset. Our second contribution is a set of experiments and evaluation of POS tagging models which starts with a baseline model for Italian and is aimed at finding the best training configuration, namely in terms of the size and combination strategy of the existing raw and annotated resources. These experiments result in (i) the first POS tagger for Corsican, reaching an accuracy of 93.38%, (ii) a quantification of the gain provided by the use of each available resource. We find that the optimal configuration uses Italian word embeddings further specialized with Corsican embeddings and trained on the largest gold corpus for Corsican available so far.

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Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models
Zhangyue Yin | Qiushi Sun | Qipeng Guo | Zhiyuan Zeng | Xiaonan Li | Tianxiang Sun | Cheng Chang | Qinyuan Cheng | Ding Wang | Xiaofeng Mou | Xipeng Qiu | Xuanjing Huang

Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances the reasoning performance of LLMs by sampling multiple reasoning chains and ensembling based on the answer frequency. However, this approach fails in scenarios where the correct answers are in the minority. We identify this as a primary factor constraining the reasoning capabilities of LLMs, a limitation that cannot be resolved solely based on the predicted answers. To address this shortcoming, we introduce a hierarchical reasoning aggregation framework AoR (Aggregation of Reasoning), which selects answers based on the evaluation of reasoning chains. Additionally, AoR incorporates dynamic sampling, adjusting the number of reasoning chains in accordance with the complexity of the task. Experimental results on a series of complex reasoning tasks show that AoR outperforms prominent ensemble methods. Further analysis reveals that AoR not only adapts various LLMs but also achieves a superior performance ceiling when compared to current methods.

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A Hierarchical Sequence-to-Set Model with Coverage Mechanism for Aspect Category Sentiment Analysis
Siyu Wang | Jianhui Jiang | Shengran Dai | Jiangtao Qiu

Aspect category sentiment analysis (ACSA) aims to simultaneously detect aspect categories and their corresponding sentiment polarities (category-sentiment pairs). Some recent studies have used pre-trained generative models to complete ACSA and achieved good results. However, for ACSA, generative models still face three challenges. First, addressing the missing predictions in ACSA is crucial, which involves accurately predicting all category-sentiment pairs within a sentence. Second, category-sentiment pairs are inherently a disordered set. Consequently, the model incurs a penalty even when its predictions are correct, but the predicted order is inconsistent with the ground truths. Third, different aspect categories should focus on relevant sentiment words, and the polarity of the aspect category should be the aggregation of the polarities of these sentiment words. This paper proposes a hierarchical generative model with a coverage mechanism using sequence-to-set learning to tackle all three challenges simultaneously. Our model’s superior performance is demonstrated through extensive experiments conducted on several datasets.

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A Hong Kong Sign Language Corpus Collected from Sign-interpreted TV News
Zhe Niu | Ronglai Zuo | Brian Mak | Fangyun Wei

This paper introduces TVB-HKSL-News, a new Hong Kong sign language (HKSL) dataset collected from a TV news program over a period of 7 months. The dataset is collected to enrich resources for HKSL and support research in large-vocabulary continuous sign language recognition (SLR) and translation (SLT). It consists of 16.07 hours of sign videos of two signers with a vocabulary of 6,515 glosses (for SLR) and 2,850 Chinese characters or 18K Chinese words (for SLT). One signer has 11.66 hours of sign videos and the other has 4.41 hours. One objective in building the dataset is to support the investigation of how well large-vocabulary continuous sign language recognition/translation can be done for a single signer given a (relatively) large amount of his/her training data, which could potentially lead to the development of new modeling methods. Besides, most parts of the data collection pipeline are automated with little human intervention; we believe that our collection method can be scaled up to collect more sign language data easily for SLT in the future for any sign languages if such sign-interpreted videos are available. We also run a SOTA SLR/SLT model on the dataset and get a baseline SLR word error rate of 34.08% and a baseline SLT BLEU-4 score of 23.58 for benchmarking future research on the dataset.

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A Hybrid Approach to Aspect Based Sentiment Analysis Using Transfer Learning
Gaurav Negi | Rajdeep Sarkar | Omnia Zayed | Paul Buitelaar

Aspect-Based Sentiment Analysis ( ABSA) aims to identify terms or multiword expressions (MWEs) on which sentiments are expressed and the sentiment polarities associated with them. The development of supervised models has been at the forefront of research in this area. However, training these models requires the availability of manually annotated datasets which is both expensive and time-consuming. Furthermore, the available annotated datasets are tailored to a specific domain, language, and text type. In this work, we address this notable challenge in current state-of-the-art ABSA research. We propose a hybrid approach for Aspect Based Sentiment Analysis using transfer learning. The approach focuses on generating weakly-supervised annotations by exploiting the strengths of both large language models (LLM) and traditional syntactic dependencies. We utilise syntactic dependency structures of sentences to complement the annotations generated by LLMs, as they may overlook domain-specific aspect terms. Extensive experimentation on multiple datasets is performed to demonstrate the efficacy of our hybrid method for the tasks of aspect term extraction and aspect sentiment classification.

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A Japanese News Simplification Corpus with Faithfulness
Toru Urakawa | Yuya Taguchi | Takuro Niitsuma | Hideaki Tamori

Text Simplification enhances the readability of texts for specific audiences. However, automated models may introduce unwanted content or omit essential details, necessitating a focus on maintaining faithfulness to the original input. Furthermore, existing simplified corpora contain instances of low faithfulness. Motivated by this issue, we present a new Japanese simplification corpus designed to prioritize faithfulness. Our collection comprises 7,075 paired sentences simplified from newspaper articles. This process involved collaboration with language education experts who followed guidelines balancing readability and faithfulness. Through corpus analysis, we confirmed that our dataset preserves the content of the original text, including personal names, dates, and city names. Manual evaluation showed that our corpus robustly maintains faithfulness to the original text, surpassing other existing corpora. Furthermore, evaluation by non-native readers confirmed its readability to the target audience. Through the experiment of fine-tuning and in-context learning, we demonstrated that our corpus enhances faithful sentence simplification.

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A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation
Xiangci Li | Linfeng Song | Lifeng Jin | Haitao Mi | Jessica Ouyang | Dong Yu

Knowledge-based, open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. Many types and sources of knowledge have previously been shown to be useful as support knowledge. Even in the era of large language models, response generation grounded in knowledge retrieved from additional up-to-date sources remains a practically important approach. While prior work using single-source knowledge has shown a clear positive correlation between the performances of knowledge selection and response generation, there are no existing multi-source datasets for evaluating support knowledge retrieval. Further, prior work has assumed that the knowledge sources available at test time are the same as during training. This unrealistic assumption unnecessarily handicaps models, as new knowledge sources can become available after a model is trained. In this paper, we present a high-quality benchmark named multi-source Wizard of Wikipedia (Ms.WoW) for evaluating multi-source dialogue knowledge selection and response generation. Unlike existing datasets, it contains clean support knowledge, grounded at the utterance level and partitioned into multiple knowledge sources. We further propose a new challenge, dialogue knowledge plug-and-play, which aims to test an already trained dialogue model on using new support knowledge from previously unseen sources in a zero-shot fashion.

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A Large Annotated Reference Corpus of New High German Poetry
Thomas Haider

This paper introduces a large annotated corpus of public domain German poetry, covering the time period from 1600 to the 1920s with 65k poems. We describe how the corpus was compiled, how it was cleaned (including duplicate detection), and how it looks now in terms of size, format, temporal distribution, and automatic annotation. Besides metadata, the corpus contains reliable annotation of tokens, syllables, part-of-speech, and meter and verse measure. Finally, we give some statistics on the annotation and an overview of other poetry corpora.

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A Lifelong Multilingual Multi-granularity Semantic Alignment Approach via Maximum Co-occurrence Probability
Xin Liu | Hongwei Sun | Shaojie Dai | Bo Lv | Youcheng Pan | Hui Wang | Yue Yu

Cross-lingual pre-training methods mask and predict tokens in multilingual text to generalize diverse multilingual information. However, due to the lack of sufficient aligned multilingual resources in the pre-training process, these methods may not fully explore the multilingual correlation of masked tokens, resulting in the limitation of multilingual information interaction. In this paper, we propose a lifelong multilingual multi-granularity semantic alignment approach, which continuously extracts massive aligned linguistic units from noisy data via a maximum co-occurrence probability algorithm. Then, the approach releases a version of the multilingual multi-granularity semantic alignment resource, supporting seven languages, namely English, Czech, German, Russian, Romanian, Hindi and Turkish. Finally, we propose how to use this resource to improve the translation performance on WMT14 18 benchmarks in twelve directions. Experimental results show an average of 0.3 1.1 BLEU improvements in all translation benchmarks. The analysis and discussion also demonstrate the superiority and potential of the proposed approach. The resource used in this work will be publicly available.

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A Lightweight Approach to a Giga-Corpus of Historical Periodicals: The Story of a Slovenian Historical Newspaper Collection
Filip Dobranić | Bojan Evkoski | Nikola Ljubešić

Preparing historical newspaper collections is a complicated endeavour, consisting of multiple steps that have to be carefully adapted to the specific content in question, including imaging, layout prediction, optical character recognition, and linguistic annotation. To address the high costs associated with the process, we present a lightweight approach to producing high-quality corpora and apply it to a massive collection of Slovenian historical newspapers from the 18th, 19th and 20th century resulting in a billion-word giga-corpus. We start with noisy OCR-ed data produced by different technologies in varying periods by the National and University Library of Slovenia. To address the inherent variability in the quality of textual data, a challenge commonly encountered in digital libraries globally, we perform a targeted post-digitisation correction procedure, coupled with a robust curation mechanism for noisy texts via language model inference. Subsequently, we subject the corrected and filtered output to comprehensive linguistic annotation, enriching the corpus with part-of-speech tags, lemmas, and named entity labels. Finally, we perform an analysis through topic modeling at the noun lemma level, along with a frequency analysis of the named entities, to confirm the viability of our corpus preparation method.

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Aligning the Norwegian UD Treebank with Entity and Coreference Information
Tollef Emil Jørgensen | Andre Kåsen

This paper presents a merged collection of entity and coreference annotated data grounded in the Universal Dependencies (UD) treebanks for the two written forms of Norwegian: Bokmål and Nynorsk. The aligned and converted corpora are the Norwegian Named Entities (NorNE) and Norwegian Anaphora Resolution Corpus (NARC). While NorNE is aligned with an older version of the treebank, NARC is misaligned and requires extensive transformation from the original annotations to the UD structure and CoNLL-U format. Here, we demonstrate the conversion and alignment processes, along with an analysis of discovered issues and errors in the data, some of which include data split overlaps in the original treebank. These procedures and the developed system may prove helpful for future work on processing and aligning data from universal dependencies. The merged corpora comprise the first Norwegian UD treebank enriched with named entities and coreference information, supporting the standardized format for the CorefUD initiative.

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Alignment before Awareness: Towards Visual Question Localized-Answering in Robotic Surgery via Optimal Transport and Answer Semantics
Zhihong Zhu | Yunyan Zhang | Xuxin Cheng | Zhiqi Huang | Derong Xu | Xian Wu | Yefeng Zheng

The visual question localized-answering (VQLA) system has garnered increasing attention due to its potential as a knowledgeable assistant in surgical education. Apart from providing text-based answers, VQLA can also pinpoint the specific region of interest for better surgical scene understanding. Although recent Transformer-based models for VQLA have obtained promising results, they (1) conduct vanilla text-to-image cross attention, leading to unidirectional and coarse-grained alignment; (2) ignore exploiting the semantics of answers to further boost performance. In this paper, we propose a novel model termed OTAS, which first introduces optimal transport to achieve bidirectional and fine-grained alignment between images and questions, enabling more precise localization. Besides, OTAS incorporates a set of learnable candidate answer embeddings to query the probability of each answer class for a given image-question pair. Through Transformer attention, the candidate answer embeddings interact with the fused features of the image-question pair to make the answer decision. Extensive experiments on two widely-used benchmark datasets demonstrate the superiority of our model over state-of-the-art methods.

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Align-to-Distill: Trainable Attention Alignment for Knowledge Distillation in Neural Machine Translation
Heegon Jin | Seonil Son | Jemin Park | Youngseok Kim | Hyungjong Noh | Yeonsoo Lee

The advent of scalable deep models and large datasets has improved the performance of Neural Machine Translation (NMT). Knowledge Distillation (KD) enhances efficiency by transferring knowledge from a teacher model to a more compact student model. However, KD approaches to Transformer architecture often rely on heuristics, particularly when deciding which teacher layers to distill from. In this paper, we introduce the “Align-to-Distill” (A2D) strategy, designed to address the feature mapping problem by adaptively aligning student attention heads with their teacher counterparts during training. The Attention Alignment Module (AAM) in A2D performs a dense head-by-head comparison between student and teacher attention heads across layers, turning the combinatorial mapping heuristics into a learning problem. Our experiments show the efficacy of A2D, demonstrating gains of up to +3.61 and +0.63 BLEU points for WMT-2022 De→Dsb and WMT-2014 En→De, respectively, compared to Transformer baselines.The code and data are available at https://github.com/ncsoft/Align-to-Distill.

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A Linguistically-Informed Annotation Strategy for Korean Semantic Role Labeling
Yige Chen | KyungTae Lim | Jungyeul Park

Semantic role labeling is an essential component of semantic and syntactic processing of natural languages, which reveals the predicate-argument structure of the language. Despite its importance, semantic role labeling for the Korean language has not been studied extensively. One notable issue is the lack of uniformity among data annotation strategies across different datasets, which often lack thorough rationales. In this study, we suggest an annotation strategy for Korean semantic role labeling that is in line with the previously proposed linguistic theories as well as the distinct properties of the Korean language. We further propose a simple yet viable conversion strategy from the Sejong verb dictionary to a CoNLL-style dataset for Korean semantic role labeling. Experiment results using a transformer-based sequence labeling model demonstrate the reliability and trainability of the converted dataset.

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Alleviating Exposure Bias in Abstractive Summarization via Sequentially Generating and Revising
Jiaxin Duan | Fengyu Lu | Junfei Liu

Abstractive summarization commonly suffers from exposure bias caused by supervised teacher-force learning, that a model predicts the next token conditioned on the accurate pre-context during training while on its preceding outputs at inference. Existing solutions bridge this gap through un- or semi-supervised holistic learning yet still leave the risk of error accumulation while generating a summary. In this paper, we attribute this problem to the limitation of unidirectional autoregressive text generation and introduce post-processing steps to alleviate it. Specifically, we reformat abstractive summarization to sequential generation and revision (SeGRe), i.e., a model in the revision phase re-inputs the generated summary and refines it by contrasting it with the source document. This provides the model additional opportunities to assess the flawed summary from a global view and thereby modify inappropriate expressions. Moreover, we train the SeGRe model with a regularized minimum-risk policy to ensure effective generation and revision. A lot of comparative experiments are implemented on two well-known datasets, exhibiting the new or matched state-of-the-art performance of SeGRe.

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ALLIES: A Speech Corpus for Segmentation, Speaker Diarization, Speech Recognition and Speaker Change Detection
Marie Tahon | Anthony Larcher | Martin Lebourdais | Fethi Bougares | Anna Silnova | Pablo Gimeno

This paper presents ALLIES, a meta corpus which gathers and extends existing French corpora collected from radio and TV shows. The corpus contains 1048 audio files for about 500 hours of speech. Agglomeration of data is always a difficult issue, as the guidelines used to collect, annotate and transcribe speech are generally different from one corpus to another. ALLIES intends to homogenize and correct speaker labels among the different files by integrated human feedback within a speaker verification system. The main contribution of this article is the design of a protocol in order to evaluate properly speech segmentation (including music and overlap detection), speaker diarization, speech transcription and speaker change detection. As part of it, a test partition has been carefully manually 1) segmented and annotated according to speech, music, noise, speaker labels with specific guidelines for overlap speech, 2) orthographically transcribed. This article also provides as a second contribution baseline results for several speech processing tasks.

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A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation
Li Yuan | Yi Cai | Haopeng Ren | Jiexin Wang

Generating coherent and credible explanations remains a significant challenge in the field of AI. In recent years, researchers have delved into the utilization of entailment trees to depict explanations, which exhibit a reasoning process of how a hypothesis is deduced from the supporting facts. However, existing models often overlook the importance of generating intermediate conclusions with logical consistency from the given facts, leading to inaccurate conclusions and undermining the overall credibility of entailment trees. To address this limitation, we propose the logical pattern memory pre-trained model (LMPM). LMPM incorporates an external memory structure to learn and store the latent representations of logical patterns, which aids in generating logically consistent conclusions. Furthermore, to mitigate the influence of logically irrelevant domain knowledge in the Wikipedia-based data, we introduce an entity abstraction approach to construct the dataset for pre-training LMPM. The experimental results highlight the effectiveness of our approach in improving the quality of entailment tree generation. By leveraging logical entailment patterns, our model produces more coherent and reasonable conclusions that closely align with the underlying premises.

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AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework
Xiang Li | Zhenyu Li | Chen Shi | Yong Xu | Qing Du | Mingkui Tan | Jun Huang

The task of financial analysis primarily encompasses two key areas: stock trend prediction and the corresponding financial question answering. Currently, machine learning and deep learning algorithms (ML&DL) have been widely applied for stock trend predictions, leading to significant progress. However, these methods fail to provide reasons for predictions, lacking interpretability and reasoning processes. Also, they can not integrate textual information such as financial news or reports. Meanwhile, large language models (LLM) have remarkable textual understanding and generation ability. But due to the scarcity of financial training datasets and limited integration with real-time knowledge, LLM still suffer from hallucinations and unable to keep up with the latest information. To tackle these challenges, we first release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. It has positive impact on training LLM for completing financial analysis. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task, which integrates retrieval-augmented generation (RAG) techniques. Extensive experiments are conducted to demonstrate the effectiveness of our framework on financial analysis.

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A Luxembourgish Corpus as a Gender Bias Evaluation Testset
Dimitra Anastasiou | Carole Blond-Hanten | Marie Gallais

According to the United Nations Development Programme, gender inequality is a metric that is composed of three dimensions: reproductive health, empowerment, and the labour market. Gender inequality is an obstacle to equal opportunities in society as a whole. In this paper we present our work-in-progress of designing and playing a physical game with digital elements. We currently conduct Conversation Analysis of transcribed speech of 58567 words and documenting bias. We also test OpenAI’s ChatGPT for bias in quiz-like gender-related questions.

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A Matter of Perspective: Building a Multi-Perspective Annotated Dataset for the Study of Literary Quality
Yuri Bizzoni | Pascale Feldkamp Moreira | Ida Marie S. Lassen | Mads Rosendahl Thomsen | Kristoffer Nielbo

Studies on literary quality have constantly stimulated the interest of critics, both in theoretical and empirical fields. To examine the perceived quality of literary works, some approaches have focused on data annotated through crowd-sourcing platforms, and others relied on available expert annotated data. In this work, we contribute to the debate by presenting a dataset collecting quality judgments on 9,000 19th and 20th century English-language literary novels by 3,150 predominantly Anglophone authors. We incorporate expert opinions and crowd-sourced annotations to allow comparative analyses between different literary quality evaluations. We also provide several textual metrics chosen for their potential connection with literary reception and engagement. While a large part of the texts is subjected to copyright, we release quality and reception measures together with stylometric and sentiment data for each of the 9,000 novels to promote future research and comparison.

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AMenDeD: Modelling Concepts by Aligning Mentions, Definitions and Decontextualised Embeddings
Amit Gajbhiye | Zied Bouraoui | Luis Espinosa Anke | Steven Schockaert

Contextualised Language Models (LM) improve on traditional word embeddings by encoding the meaning of words in context. However, such models have also made it possible to learn high-quality decontextualised concept embeddings. Three main strategies for learning such embeddings have thus far been considered: (i) fine-tuning the LM to directly predict concept embeddings from the name of the concept itself, (ii) averaging contextualised representations of mentions of the concept in a corpus, and (iii) encoding definitions of the concept. As these strategies have complementary strengths and weaknesses, we propose to learn a unified embedding space in which all three types of representations can be integrated. We show that this allows us to outperform existing approaches in tasks such as ontology completion, which heavily depends on access to high-quality concept embeddings. We furthermore find that mentions and definitions are well-aligned in the resulting space, enabling tasks such as target sense verification, even without the need for any fine-tuning.

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A Multi-Label Dataset of French Fake News: Human and Machine Insights
Benjamin Icard | François Maine | Morgane Casanova | Géraud Faye | Julien Chanson | Guillaume Gadek | Ghislain Atemezing | François Bancilhon | Paul Égré

We present a corpus of 100 documents, named OBSINFOX, selected from 17 sources of French press considered unreliable by expert agencies, annotated using 11 labels by 8 annotators. By collecting more labels than usual, by more annotators than is typically done, we can identify features that humans consider as characteristic of fake news, and compare them to the predictions of automated classifiers. We present a topic and genre analysis using Gate Cloud, indicative of the prevalence of satire-like text in the corpus. We then use the subjectivity analyzer VAGO, and a neural version of it, to clarify the link between ascriptions of the label Subjective and ascriptions of the label Fake News. The annotated dataset is available online at the following url: https://github.com/obs-info/obsinfox Keywords: Fake News, Multi-Labels, Subjectivity, Vagueness, Detail, Opinion, Exaggeration, French Press

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A Multi-layered Approach to Physical Commonsense Understanding: Creation and Evaluation of an Italian Dataset
Giulia Pensa | Begoña Altuna | Itziar Gonzalez-Dios

In this paper, we explore physical commonsense reasoning of large language models (LLMs) and propose a specific methodology to evaluate low-level understanding of the physical world. Specifically, the goal is to create a test set to analyze physical commonsense reasoning in large language models for Italian and focus on a trustworthy analysis of the results. To that end, we present a tiered Italian dataset, called Graded Italian Annotated dataset (GITA), written and thoroughly annotated by a professional linguist, which allows us to concentrate on three different levels of commonsense understanding. Moreover, we create a semi-automated system to complete the accurate annotation of the dataset. We also validate our dataset by carrying out three tasks with a multilingual model (XLM-RoBERTa) and propose a qualitative analysis of the results. We found out that, although the model may perform at high-level classification tasks, its easoning is inconsistent and unverifiable, since it does not capture intermediate evidence.

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A Multilingual Parallel Corpus for Aromanian
Iulia Petrariu | Sergiu Nisioi

We report the creation of the first high-quality corpus of Aromanian - an endangered Romance language spoken in the Balkans - and the equivalent sentence-aligned translations into Romanian, English, and French. The corpus is released publicly using several orthographic standards and consists in short stories collected in the ‘70s in Romania. Additionally, we provide an corpus-based analysis of Aromanian linguistic particularities and the overall demographic and political context which impacts the contemporary development of the language.

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A Multimodal French Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation
Cécile Macaire | Chloé Dion | Jordan Arrigo | Claire Lemaire | Emmanuelle Esperança-Rodier | Benjamin Lecouteux | Didier Schwab

The automatic translation of spoken language into pictogram units can facilitate communication involving individuals with language impairments. However, there is no established translation formalism or publicly available datasets for training end-to-end speech translation systems. This paper introduces the first aligned speech, text, and pictogram translation dataset ever created in any language. We provide a French dataset that contains 230 hours of speech resources. We create a rule-based pictogram grammar with a restricted vocabulary and include a discussion of the strategic decisions involved. It takes advantage of an in-depth linguistic study of resources taken from the ARASAAC website. We validate these rules through multiple post-editing phases by expert annotators. The constructed dataset is then used to experiment with a Speech-to-Pictogram cascade model, which employs state-of-the-art Automatic Speech Recognition models. The dataset is freely available under a non-commercial licence. This marks a starting point to conduct research into the automatic translation of speech into pictogram units.

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A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation
Yunxin Li | Baotian Hu | Wenhan Luo | Lin Ma | Yuxin Ding | Min Zhang

In this paper, we propose a new setting for generating product descriptions from images, augmented by marketing keywords. It leverages the combined power of visual and textual information to create descriptions that are more tailored to the unique features of products. For this setting, previous methods utilize visual and textual encoders to encode the image and keywords and employ a language model-based decoder to generate the product description. However, the generated description is often inaccurate and generic since same-category products have similar copy-writings, and optimizing the overall framework on large-scale samples makes models concentrate on common words yet ignore the product features. To alleviate the issue, we present a simple and effective Multimodal In-Context Tuning approach, named ModICT, which introduces a similar product sample as the reference and utilizes the in-context learning capability of language models to produce the description. During training, we keep the visual encoder and language model frozen, focusing on optimizing the modules responsible for creating multimodal in-context references and dynamic prompts. This approach preserves the language generation prowess of large language models (LLMs), facilitating a substantial increase in description diversity. To assess the effectiveness of ModICT across various language model scales and types, we collect data from three distinct product categories within the E-commerce domain. Extensive experiments demonstrate that ModICT significantly improves the accuracy (by up to 3.3% on Rouge-L) and diversity (by up to 9.4% on D-5) of generated results compared to conventional methods. Our findings underscore the potential of ModICT as a valuable tool for enhancing the automatic generation of product descriptions in a wide range of applications. Data and code are at https://github.com/HITsz-TMG/Multimodal-In-Context-Tuning

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A Multi-Task Transformer Model for Fine-grained Labelling of Chest X-Ray Reports
Yuanyi Zhu | Maria Liakata | Giovanni Montana

Precise understanding of free-text radiology reports through localised extraction of clinical findings can enhance medical imaging applications like computer-aided diagnosis. We present a new task, that of segmenting radiology reports into topically meaningful passages (segments) and a transformer-based model that both segments reports into semantically coherent segments and classifies each segment using a set of 37 radiological abnormalities, thus enabling fine-grained analysis. This contrasts with prior work that performs classification on full reports without localisation. Trained on over 2.7 million unlabelled chest X-ray reports and over 28k segmented and labelled reports, our model achieves state-of-the-art performance on report segmentation (0.0442 WinDiff) and multi-label classification (0.84 report-level macro F1) over 37 radiological labels and 8 NLP-specific labels. This work establishes new benchmarks for fine-grained understanding of free-text radiology reports, with precise localisation of semantics unlocking new opportunities to improve computer vision model training and clinical decision support. We open-source our annotation tool, model code and pretrained weights to encourage future research.

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Analysis of Sensation-transfer Dialogues in Motorsports
Takeru Isaka | Atsushi Otsuka | Iwaki Toshima

Clarifying the effects of subjective ideas on group performance is essential for future dialogue systems to improve mutual understanding among humans and group creativity. However, there has been little focus on dialogue research on quantitatively analyzing the effects of the quality and quantity of subjective information contained in dialogues on group performance. We hypothesize that the more subjective information interlocutors exchange, the better the group performance in collaborative work. We collected dialogues between drivers and engineers in motorsports when deciding how the car should be tuned as a suitable case to verify this hypothesis. Our analysis suggests that the greater the amount of subjective information (which we defined as “sensation”) in the driver’s utterances, the greater the race performance and driver satisfaction with the car’s tuning. The results indicate that it is essential for the development of dialogue research to create a corpus of situations that require high performance through collaboration among experts with different backgrounds but who have mastered their respective fields.

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Analysis on Unsupervised Acquisition Process of Bilingual Vocabulary through Iterative Back-Translation
Takuma Tanigawa | Tomoyosi Akiba | Hajime Tsukada

In this paper, we investigate how new bilingual vocabulary is acquired through Iterative Back-Translation (IBT), which is known as a data augmentation method for machine translation from monolingual data of both source and target languages. To reveal the acquisition process, we first identify the word translation pairs in test data that do not exist in a bilingual data but do only in two monolingual data, then observe how many pairs are successfully translated by the translation model trained through IBT. We experimented on it with domain adaptation settings on two language pairs. Our experimental evaluation showed that more than 60% of the new bilingual vocabulary is successfully acquired through IBT along with the improvement in the translation quality in terms of BLEU. It also revealed that new bilingual vocabulary was gradually acquired by repeating IBT iterations. From the results, we present our hypothesis on the process of new bilingual vocabulary acquisition where the context of the words plays a critical role in the success of the acquisition.

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Analyzing Chain-of-thought Prompting in Black-Box Large Language Models via Estimated V-information
Zecheng Wang | Chunshan Li | Zhao Yang | Qingbin Liu | Yanchao Hao | Xi Chen | Dianhui Chu | Dianbo Sui

Chain-of-Thought (CoT) prompting combined with large language models (LLM) has shown great potential in improving performance on challenging reasoning tasks. While understanding why CoT prompting is effective is crucial for the application and improvement of CoT prompting, few studies have addressed this issue. Besides, almost no prior work has conducted theoretical analysis on CoT prompting in the context of black-box models. In this paper, we approach the analysis of CoT prompting in black-box LLMs from an information-theoretic perspective. Specifically, we propose a new metric, EPVI (Estimated Pointwise V-Information), which extends the concept of pointwise V-information to black-box models, quantifying the label-relevant new information introduced by CoT prompting beyond the pre-existing information in the input. Based on this, we conduct a series of experiments at both the task and instance levels to analyze CoT prompting, demonstrating that the effectiveness of CoT prompting can be attributed to its capacity to influence the difficulty of model inference by augmenting or reducing the model-usable information. Furthermore, we show that selecting high-quality demonstrations of CoT reasoning based on EPVI can improve the downstream performance of reasoning tasks.

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Analyzing Effects of Learning Downstream Tasks on Moral Bias in Large Language Models
Niklas Kiehne | Alexander Ljapunov | Marc Bätje | Wolf-Tilo Balke

Pre-training and fine-tuning large language models (LMs) is currently the state-of-the-art methodology for enabling data-scarce downstream tasks. However, the derived models still tend to replicate and perpetuate social biases. To understand this process in more detail, this paper investigates the actual effects of learning downstream tasks on moral bias in LMs. We develop methods to assess the agreement of LMs to explicitly codified norms in both pre-training and fine-tuning stages. Even if a pre-trained foundation model exhibits consistent norms, we find that introducing downstream tasks may indeed lead to unexpected inconsistencies in norm representation. Specifically, we observe two phenomena during fine-tuning across both masked and causal LMs: (1) pre-existing moral bias may be mitigated or amplified even when presented with opposing views and (2) prompt sensitivity may be negatively impacted. We provide empirical evidence of models deteriorating into conflicting states, where contradictory answers can easily be triggered by slight modifications in the input sequence. Our findings thus raise concerns about the general ability of LMs to mitigate moral biases effectively.

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Analyzing Homonymy Disambiguation Capabilities of Pretrained Language Models
Lorenzo Proietti | Stefano Perrella | Simone Tedeschi | Giulia Vulpis | Leonardo Lavalle | Andrea Sanchietti | Andrea Ferrari | Roberto Navigli

Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP), aiming to assign the correct meaning (sense) to a word in context. However, traditional WSD systems rely on WordNet as the underlying sense inventory, often differentiating meticulously between subtle nuances of word meanings, which may lead to excessive complexity and reduced practicality of WSD systems in today’s NLP. Indeed, current Pretrained Language Models (PLMs) do seem to be able to perform disambiguation, but it is not clear to what extent, or to what level of granularity, they actually operate. In this paper, we address these points and, firstly, introduce a new large-scale resource that leverages homonymy relations to systematically cluster WordNet senses, effectively reducing the granularity of word senses to a very coarse-grained level; secondly, we use this resource to train Homonymy Disambiguation systems and investigate whether PLMs are inherently able to differentiate coarse-grained word senses. Our findings demonstrate that, while state-of-the-art models still struggle to choose the correct fine-grained meaning of a word in context, Homonymy Disambiguation systems are able to differentiate homonyms with up to 95% accuracy scores even without fine-tuning the underlying PLM. We release our data and code at https://github.com/SapienzaNLP/homonymy-wsd.

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Analyzing Interpretability of Summarization Model with Eye-gaze Information
Fariz Ikhwantri | Hiroaki Yamada | Takenobu Tokunaga

Interpretation methods provide saliency scores indicating the importance of input words for neural summarization models. Prior work has analyzed models by comparing them to human behavior, often using eye-gaze as a proxy for human attention in reading tasks such as classification. This paper presents a framework to analyze the model behavior in summarization by comparing it to human summarization behavior using eye-gaze data. We examine two research questions: RQ1) whether model saliency conforms to human gaze during summarization and RQ2) how model saliency and human gaze affect summarization performance. For RQ1, we measure conformity by calculating the correlation between model saliency and human fixation counts. For RQ2, we conduct ablation experiments removing words/sentences considered important by models or humans. Experiments on two datasets with human eye-gaze during summarization partially confirm that model saliency aligns with human gaze (RQ1). However, ablation experiments show that removing highly-attended words/sentences from the human gaze does not significantly degrade performance compared with the removal by the model saliency (RQ2).

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Analyzing Large Language Models’ Capability in Location Prediction
Zhaomin Xiao | Yan Huang | Eduardo Blanco

In this paper, we investigate and evaluate large language models’ capability in location prediction. We present experimental results with four models—FLAN-T5, FLAN-UL2, FLAN-Alpaca, and ChatGPT—in various instruction finetuning and exemplar settings. We analyze whether taking into account the context—tweets published before and after the tweet mentioning a location—is beneficial. Additionally, we conduct an ablation study to explore whether instruction modification is beneficial. Lastly, our qualitative analysis sheds light on the errors made by the best-performing model.

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Analyzing Occupational Distribution Representation in Japanese Language Models
Katsumi Ibaraki | Winston Wu | Lu Wang | Rada Mihalcea

Recent advances in large language models (LLMs) have enabled users to generate fluent and seemingly convincing text. However, these models have uneven performance in different languages, which is also associated with undesirable societal biases toward marginalized populations. Specifically, there is relatively little work on Japanese models, despite it being the thirteenth most widely spoken language. In this work, we first develop three Japanese language prompts to probe LLMs’ understanding of Japanese names and their association between gender and occupations. We then evaluate a variety of English, multilingual, and Japanese models, correlating the models’ outputs with occupation statistics from the Japanese Census Bureau from the last 100 years. Our findings indicate that models can associate Japanese names with the correct gendered occupations when using constrained decoding. However, with sampling or greedy decoding, Japanese language models have a preference for a small set of stereotypically gendered occupations, and multilingual models, though trained on Japanese, are not always able to understand Japanese prompts.

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Analyzing Symptom-based Depression Level Estimation through the Prism of Psychiatric Expertise
Navneet Agarwal | Kirill Milintsevich | Lucie Metivier | Maud Rotharmel | Gaël Dias | Sonia Dollfus

The ever-growing number of people suffering from mental distress has motivated significant research initiatives towards automated depression estimation. Despite the multidisciplinary nature of the task, very few of these approaches include medical professionals in their research process, thus ignoring a vital source of domain knowledge. In this paper, we propose to bring the domain experts back into the loop and incorporate their knowledge within the gold-standard DAIC-WOZ dataset. In particular, we define a novel transformer-based architecture and analyse its performance in light of our expert annotations. Overall findings demonstrate a strong correlation between the psychological tendencies of medical professionals and the behavior of the proposed model, which additionally provides new state-of-the-art results.

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Analyzing the Dynamics of Climate Change Discourse on Twitter: A New Annotated Corpus and Multi-Aspect Classification
Shuvam Shiwakoti | Surendrabikram Thapa | Kritesh Rauniyar | Akshyat Shah | Aashish Bhandari | Usman Naseem

The discourse surrounding climate change on social media platforms has emerged as a significant avenue for understanding public sentiments, perspectives, and engagement with this critical global issue. The unavailability of publicly available datasets, coupled with ignoring the multi-aspect analysis of climate discourse on social media platforms, has underscored the necessity for further advancement in this area. To address this gap, in this paper, we present an extensive exploration of the intricate realm of climate change discourse on Twitter, leveraging a meticulously annotated ClimaConvo dataset comprising 15,309 tweets. Our annotations encompass a rich spectrum, including aspects like relevance, stance, hate speech, the direction of hate, and humor, offering a nuanced understanding of the discourse dynamics. We address the challenges inherent in dissecting online climate discussions and detail our comprehensive annotation methodology. In addition to annotations, we conduct benchmarking assessments across various algorithms for six tasks: relevance detection, stance detection, hate speech identification, direction and target, and humor analysis. This assessment enhances our grasp of sentiment fluctuations and linguistic subtleties within the discourse. Our analysis extends to exploratory data examination, unveiling tweet distribution patterns, stance prevalence, and hate speech trends. Employing sophisticated topic modeling techniques uncovers underlying thematic clusters, providing insights into the diverse narrative threads woven within the discourse. The findings present a valuable resource for researchers, policymakers, and communicators seeking to navigate the intricacies of climate change discussions. The dataset and resources for this paper are available at https://github.com/shucoll/ClimaConvo.

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Analyzing the Performance of Large Language Models on Code Summarization
Rajarshi Haldar | Julia Hockenmaier

Large language models (LLMs) such as Llama 2 perform very well on tasks that involve both natural language and source code, particularly code summarization and code generation. We show that for the task of code summarization, the performance of these models on individual examples often depends on the amount of (subword) token overlap between the code and the corresponding reference natural language descriptions in the dataset. This token overlap arises because the reference descriptions in standard datasets (corresponding to docstrings in large code bases) are often highly similar to the names of the functions they describe. We also show that this token overlap occurs largely in the function names of the code and compare the relative performance of these models after removing function names versus removing code structure. We also show that using multiple evaluation metrics like BLEU and BERTScore gives us very little additional insight since these metrics are highly correlated with each other.

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Analyzing the Understanding of Morphologically Complex Words in Large Language Models
Marion Weller-Di Marco | Alexander Fraser

We empirically study the ability of a Large Language Model (gpt-3.5-turbo-instruct) to understand morphologically complex words. In our experiments, we looked at a variety of tasks to analyse German compounds with regard to compositional word formation and derivation, such as identifying the head noun of existing and novel compounds, identifying the shared verb stem between two words, or recognizing words constructed with inappropriately used derivation morphemes as invalid. Our results show that the language model is generally capable of solving most tasks, except for the task of identifying ill-formed word forms. While the model demonstrated a good overall understanding of complex words and their word-internal structure, the results also suggest that there is no formal knowledge of derivational rules, but rather an interpretation of the observed word parts to derive the meaning of a word.

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An Argument for Symmetric Coordination from Dependency Length Minimization: A Replication Study
Adam Przepiórkowski | Magdalena Borysiak | Adam Głowacki

It is well known that left conjuncts tend to be shorter in English coordinate structures. On the basis of Penn Treebank, Przepiórkowski and Woźniak 2023 (in ACL 2023 proceedings) show that this tendency depends on the difference between lengths of conjuncts: the larger the difference, the stronger the tendency for the shorter conjunct to occur on the left. However, this dynamics is observed only when the governor of the coordinate structure is on the left of the coordination (e.g., “Bring apples and oranges!”) or when it is absent (e.g., “Come and sing!”), and not when it is on the right (e.g., “Apples and oranges fell”). Given the principle of Dependency Length Minimization, this turns out to provide an argument for the symmetric structure of coordination. We replicate and sharpen this result on the basis of a much larger dataset: parts of the COCA corpus parsed with Stanza. We also investigate the dependence of this result on the assumed unit of length (word vs. character) and on genre.

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A Natural Approach for Synthetic Short-Form Text Analysis
Ruiting Shao | Ryan Schwarz | Christopher Clifton | Edward Delp

Detecting synthetically generated text in the wild has become increasingly difficult with advances in Natural Language Generation techniques and the proliferation of freely available Large Language Models (LLMs). Social media and news sites can be flooded with synthetically generated misinformation via tweets and posts while authentic users can inadvertently spread this text via shares and retweets. Most modern natural language processing techniques designed to detect synthetically generated text focus primarily on long-form content, such as news articles, or incorporate stylometric characteristics and metadata during their analysis. Unfortunately, for short form text like tweets, this information is often unavailable, usually detached from its original source, displayed out of context, and is often too short or informal to yield significant information from stylometry. This paper proposes a method of detecting synthetically generated tweets via a Transformer architecture and incorporating unique style-based features. Additionally, we have created a new dataset consisting of human-generated and Large Language Model generated tweets for 4 topics and another dataset consisting of tweets paraphrased by 3 different paraphrase models.

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An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation
Ahmet Gunduz | Kamer Ali Yuksel | Kareem Darwish | Golara Javadi | Fabio Minazzi | Nicola Sobieski | Sébastien Bratières

Data availability is crucial for advancing artificial intelligence applications, including voice-based technologies. As content creation, particularly in social media, experiences increasing demand, translation and text-to-speech (TTS) technologies have become essential tools. Notably, the performance of these TTS technologies is highly dependent on the quality of the training data, emphasizing the mutual dependence of data availability and technological progress. This paper introduces an end-to-end tool to generate high-quality datasets for text-to-speech (TTS) models to address this critical need for high-quality data. The contributions of this work are manifold and include: the integration of language-specific phoneme distribution into sample selection, automation of the recording process, automated and human-in-the-loop quality assurance of recordings, and processing of recordings to meet specified formats. The proposed application aims to streamline the dataset creation process for TTS models through these features, thereby facilitating advancements in voice-based technologies.

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Anchor and Broadcast: An Efficient Concept Alignment Approach for Evaluation of Semantic Graphs
Haibo Sun | Nianwen Xue

In this paper, we present AnCast, an intuitive and efficient tool for evaluating graph-based meaning representations (MR). AnCast implements evaluation metrics that are well understood in the NLP community, and they include concept F1, unlabeled relation F1, labeled relation F1, and weighted relation F1. The efficiency of the tool comes from a novel anchor broadcast alignment algorithm that is not subject to the trappings of local maxima. We show through experimental results that the AnCast score is highly correlated with the widely used Smatch score, but its computation takes only about 40% the time.

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An Effective Span-based Multimodal Named Entity Recognition with Consistent Cross-Modal Alignment
Yongxiu Xu | Hao Xu | Heyan Huang | Shiyao Cui | Minghao Tang | Longzheng Wang | Hongbo Xu

With the increasing availability of multimodal content on social media, consisting primarily of text and images, multimodal named entity recognition (MNER) has gained a wide-spread attention. A fundamental challenge of MNER lies in effectively aligning different modalities. However, the majority of current approaches rely on word-based sequence labeling framework and align the image and text at inconsistent semantic levels (whole image-words or regions-words). This misalignment may lead to inferior entity recognition performance. To address this issue, we propose an effective span-based method, named SMNER, which achieves a more consistent multimodal alignment from the perspectives of information-theoretic and cross-modal interaction, respectively. Specifically, we first introduce a cross-modal information bottleneck module for the global-level multimodal alignment (whole image-whole text). This module aims to encourage the semantic distribution of the image to be closer to the semantic distribution of the text, which can enable the filtering out of visual noise. Next, we introduce a cross-modal attention module for the local-level multimodal alignment (regions-spans), which captures the correlations between regions in the image and spans in the text, enabling a more precise alignment of the two modalities. Extensive ex- periments conducted on two benchmark datasets demonstrate that SMNER outperforms the state-of-the-art baselines.

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An Empirical Study of Synthetic Data Generation for Implicit Discourse Relation Recognition
Kazumasa Omura | Fei Cheng | Sadao Kurohashi

Implicit Discourse Relation Recognition (IDRR), which is the task of recognizing the semantic relation between given text spans that do not contain overt clues, is a long-standing and challenging problem. In particular, the paucity of training data for some error-prone discourse relations makes the problem even more challenging. To address this issue, we propose a method of generating synthetic data for IDRR using a large language model. The proposed method is summarized as two folds: extraction of confusing discourse relation pairs based on false negative rate and synthesis of data focused on the confusion. The key points of our proposed method are utilizing a confusion matrix and adopting two-stage prompting to obtain effective synthetic data. According to the proposed method, we generated synthetic data several times larger than training examples for some error-prone discourse relations and incorporated it into training. As a result of experiments, we achieved state-of-the-art macro-F1 performance thanks to the synthetic data without sacrificing micro-F1 performance and demonstrated its positive effects especially on recognizing some infrequent discourse relations.

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An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation
Supryadi Supryadi | Leiyu Pan | Deyi Xiong

Massively multilingual neural machine translation (MMNMT) has been proven to enhance the translation quality of low-resource languages. In this paper, we empirically investigate the translation robustness of Indonesian-Chinese translation in the face of various naturally occurring noise. To assess this, we create a robustness evaluation benchmark dataset for Indonesian-Chinese translation. This dataset is automatically translated into Chinese using four NLLB-200 models of different sizes. We conduct both automatic and human evaluations. Our in-depth analysis reveal the correlations between translation error types and the types of noise present, how these correlations change across different model sizes, and the relationships between automatic evaluation indicators and human evaluation indicators. The dataset is publicly available at https://github.com/tjunlp-lab/ID-ZH-MTRobustEval.

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An Evaluation of Croatian ASR Models for Čakavian Transcription
Shulin Zhang | John Hale | Margaret Renwick | Zvjezdana Vrzić | Keith Langston

To assist in the documentation of Čakavian, an endangered language variety closely related to Croatian, we test four currently available ASR models that are trained with Croatian data and assess their performance in the transcription of Čakavian audio data. We compare the models’ word error rates, analyze the word-level error types, and showcase the most frequent Deletion and Substitution errors. The evaluation results indicate that the best-performing system for transcribing Čakavian was a CTC-based variant of the Conformer model.

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An Event-based Abductive Learning for Hard Time-sensitive Question Answering
Shaojuan Wu | Jitong Li | Xiaowang Zhang | Zhiyong Feng

Time-Sensitive Question Answering (TSQA) is to answer questions qualified for a certain timestamp based on the given document. It is split into easy and hard modes depending on whether the document contain time qualifiers mentioned in the question. While existing models have performed well on easy mode, their performance is significant reduced for answering hard time-sensitive questions, whose time qualifiers are implicit in the document. An intuitive idea is to match temporal events in the given document by treating time-sensitive question as a temporal event of missing objects. However, not all temporal events extracted from the document have explicit time qualifiers. In this paper, we propose an Event-AL framework, in which a graph pruning model is designed to locate the timespan of implicit temporal events by capturing temporal relation between events. Moreover, we present an abductive reasoning module to determine proper objects while providing explanations. Besides, as the same relation may be scattered throughout the document in diverse expressions, a relation-based prompt is introduced to instructs LLMs in extracting candidate temporal events. We conduct extensive experiment and results show that Event-AL outperforms strong baselines for hard time-sensitive questions, with a 12.7% improvement in EM scores. In addition, it also exhibits great superiority for multi-answer and beyond hard time-sensitive questions.

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A New Massive Multilingual Dataset for High-Performance Language Technologies
Ona de Gibert | Graeme Nail | Nikolay Arefyev | Marta Bañón | Jelmer van der Linde | Shaoxiong Ji | Jaume Zaragoza-Bernabeu | Mikko Aulamo | Gema Ramírez-Sánchez | Andrey Kutuzov | Sampo Pyysalo | Stephan Oepen | Jörg Tiedemann

We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of ≈ 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.

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An LCF-IDF Document Representation Model Applied to Long Document Classification
Renzo Arturo Alva Principe | Nicola Chiarini | Marco Viviani

A document representation model that has been used for years in NLP and Text Mining tasks is TF-IDF (Term Frequency-Inverse Document Frequency). This model is indeed effective for various tasks like Information Retrieval and Document Classification. However, it may fall short when it comes to capturing the deeper semantic and contextual meaning of a text, which is where Transformer-based Pre-trained Language Models (PLMs) such as BERT have been gaining significant traction in recent years. Despite this, these models also face specific challenges related to Transformers and their attention mechanism limits, especially when dealing with long documents. Therefore, this paper proposes a novel approach to exploit the advantages of the TF-IDF representation while incorporating semantic context, by introducing a Latent Concept Frequency-Inverse Document Frequency (LCF-IDF) document representation model. Its effectiveness is tested with respect to the Long Document Classification task. The results obtained show promising performance of the proposed solution compared to TF-IDF and BERT-like representation models, including those specifically for long documents such as Longformer as well as those designed for particular domains, especially when it comes to Single Label Multi-Class (SLMC) classification.

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An LLM-Enhanced Adversarial Editing System for Lexical Simplification
Keren Tan | Kangyang Luo | Yunshi Lan | Zheng Yuan | Jinlong Shu

Lexical Simplification (LS) aims to simplify text at the lexical level. Existing methods rely heavily on annotated data, making it challenging to apply in low-resource scenarios. In this paper, we propose a novel LS method without parallel corpora. This method employs an Adversarial Editing System with guidance from a confusion loss and an invariance loss to predict lexical edits in the original sentences. Meanwhile, we introduce an innovative LLM-enhanced loss to enable the distillation of knowledge from Large Language Models (LLMs) into a small-size LS system. From that, complex words within sentences are masked and a Difficulty-aware Filling module is crafted to replace masked positions with simpler words. At last, extensive experimental results and analyses on three benchmark LS datasets demonstrate the effectiveness of our proposed method.

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AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports
Lukas Lange | Marc Müller | Ghazaleh Haratinezhad Torbati | Dragan Milchevski | Patrick Grau | Subhash Chandra Pujari | Annemarie Friedrich

Monitoring the threat landscape to be aware of actual or potential attacks is of utmost importance to cybersecurity professionals. Information about cyber threats is typically distributed using natural language reports. Natural language processing can help with managing this large amount of unstructured information, yet to date, the topic has received little attention. With this paper, we present AnnoCTR, a new CC-BY-SA-licensed dataset of cyber threat reports. The reports have been annotated by a domain expert with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. Entities and concepts are linked to Wikipedia and the MITRE ATT&CK knowledge base, the most widely-used taxonomy for classifying types of attacks. Prior datasets linking to MITRE ATT&CK either provide a single label per document or annotate sentences out-of-context; our dataset annotates entire documents in a much finer-grained way. In an experimental study, we model the annotations of our dataset using state-of-the-art neural models. In our few-shot scenario, we find that for identifying the MITRE ATT&CK concepts that are mentioned explicitly or implicitly in a text, concept descriptions from MITRE ATT&CK are an effective source for training data augmentation.

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Annotate Chinese Aspect with UMR——a Case Study on the Liitle Prince
Sijia Ge | Zilong Li | Alvin Po-Chun Chen | Guanchao Wang

Aspect is a valuable tool for determining the perspective from which an event is observed, allowing for viewing both at the situation and viewpoint level. Uniform Meaning Representation (UMR) seeks to provide a standard, typologically-informed representation of aspects across languages. It employs an aspectual lattice to adapt to different languages and design values that encompass both viewpoint aspect and situation aspects. In the context of annotating the Chinese version of The Little Prince, we paid particular attention to the interactions between aspect values and aspect markers and we also want to know the annotation effectiveness and challenges under the UMR aspectual lattice. During our annotation process, we identified the relationships between aspectual markers and labels. We further categorized and analyzed complex examples that led to low inter-annotator agreement. The factors contributing to disagreement among annotators included the interpretations of lexical semantics, implications, and the influence of aspectual markers, which is related to the inclination of the situation aspect and the exclusivity between the two aspects’ perspectives. Overall, our work sheds light on the challenges of aspect annotation in Chinese and highlights the need for more comprehensive guidelines.

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Annotate the Way You Think: An Incremental Note Generation Framework for the Summarization of Medical Conversations
Longxiang Zhang | Caleb D. Hart | Susanne Burger | Thomas Schaaf

The scarcity of public datasets for the summarization of medical conversations has been a limiting factor for advancing NLP research in the healthcare domain, and the structure of the existing data is largely limited to the simple format of conversation-summary pairs. We therefore propose a novel Incremental Note Generation (ING) annotation framework capable of greatly enriching summarization datasets in the healthcare domain and beyond. Our framework is designed to capture the human summarization process via an annotation task by instructing the annotators to first incrementally create a draft note as they accumulate information through a conversation transcript (Generation) and then polish the draft note into a reference note (Rewriting). The annotation results include both the reference note and a comprehensive editing history of the draft note in tabular format. Our pilot study on the task of SOAP note generation showed reasonable consistency between four expert annotators, established a solid baseline for quantitative targets of inter-rater agreement, and demonstrated the ING framework as an improvement over the traditional annotation process for future modeling of summarization.

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Annotating Chinese Word Senses with English WordNet: A Practice on OntoNotes Chinese Sense Inventories
Hongzhi Xu | Jingxia Lin | Sameer Pradhan | Mitchell Marcus | Ming Liu

In this paper, we present our exploration of annotating Chinese word senses using English WordNet synsets, with examples extracted from OntoNotes Chinese sense inventories. Given a target word along with the example that contains it, the annotators select a WordNet synset that best describes the meaning of the target word in the context. The result demonstrates an inter-annotator agreement of 38% between two annotators. We delve into the instances of disagreement by comparing the two annotated synsets, including their positions within the WordNet hierarchy. The examination reveals intriguing patterns among closely related synsets, shedding light on similar concepts represented within the WordNet structure. The data offers as an indirect linking of Chinese word senses defined in OntoNotes Chinese sense inventories to WordNet sysnets, and thus promotes the value of the OntoNotes corpus. Compared to a direct linking of Chinese word senses to WordNet synsets, the example-based annotation has the merit of not being affected by inaccurate sense definitions and thus offers a new way of mapping WordNets of different languages. At the same time, the annotated data also serves as a valuable linguistic resource for exploring potential lexical differences between English and Chinese, with potential contributions to the broader understanding of cross-linguistic semantic mapping

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Annotating Customer-Oriented Behaviour in Call Centre Sales Dialogues
Jutta Stock | Volha Petukhova | Dietrich Klakow

Customer-oriented behaviour (COB) plays an important role in call centre interactions, particularly in the context of successful sales negotiation. However, the evaluation of COB in customer-agent conversations often lacks clarity in its definition and robust computational assessment methods. This paper addresses these challenges by presenting a comprehensive conceptual and empirical framework. We conducted multidimensional dialogue act annotations on authentic call centre interactions using the ISO 24617-2 taxonomy, capturing the multifaceted nature of these interactions. This process led to the identification of relevant dialogue act categories, proposed extensions concerning relationship-building aspects, and derived corpus statistics. The findings highlight specific facets of COB that positively impact on Customer Satisfaction (CS), as determined through correlation analysis. Additionally, we delved into the dependencies between COB and feedback acts, leveraging the hierarchical structure of the DIT++ model. This framework improves our understanding of the dynamics shaping sales strategies in call centres and holds promise for practical applications in optimising customer-agent interactions.

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Annotation and Classification of Relevant Clauses in Terms-and-Conditions Contracts
Pietro Giovanni Bizzaro | Elena Della Valentina | Maurizio Napolitano | Nadia Mana | Massimo Zancanaro

In this paper, we propose a new annotation scheme to classify different types of clauses in Terms-and-Conditions contracts with the ultimate goal of supporting legal experts to quickly identify and assess problematic issues in this type of legal documents. To this end, we built a small corpus of Terms-and-Conditions contracts and finalized an annotation scheme of 14 categories, eventually reaching an inter-annotator agreement of 0.92. Then, for 11 of them, we experimented with binary classification tasks using few-shot prompting with a multilingual T5 and two fine-tuned versions of two BERT-based LLMs for Italian. Our experiments showed the feasibility of automatic classification of our categories by reaching accuracies ranging from .79 to .95 on validation tasks.

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Annotation of Japanese Discourse Relations Focusing on Concessive Inferences
Ai Kubota | Takuma Sato | Takayuki Amamoto | Ryota Akiyoshi | Koji Mineshima

In this study, we focus on the inference presupposed in the concessive discourse relation and present the discourse relation annotation for the Japanese connectives ‘nagara’ and ‘tsutsu’, both of which have two usages: Synchronous and Concession, just like English while. We also present the annotation for ‘tokorode’, which is ambiguous in three ways: Temporal, Location, and Concession. While corpora containing concessive discourse relations already exist, the distinctive feature of our study is that it aims to identify the concessive inferential relations by writing out the implicit presupposed inferences. In this paper, we report on the annotation methodology and its results, as well as the characteristics of concession that became apparent during annotation.

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Annotation of Transition-Relevance Places and Interruptions for the Description of Turn-Taking in Conversations in French Media Content
Rémi Uro | Marie Tahon | Jane Wottawa | David Doukhan | Albert Rilliard | Antoine Laurent

Few speech resources describe interruption phenomena, especially for TV and media content. The description of these phenomena may vary across authors: it thus leaves room for improved annotation protocols. We present an annotation of Transition-Relevance Places (TRP) and Floor-Taking event types on an existing French TV and Radio broadcast corpus to facilitate studies of interruptions and turn-taking. Each speaker change is annotated with the presence or absence of a TRP, and a classification of the next-speaker floor-taking as Smooth, Backchannel or different types of turn violations (cooperative or competitive, successful or attempted interruption). An inter-rater agreement analysis shows such annotations’ moderate to substantial reliability. The inter-annotator agreement for TRP annotation reaches κ=0.75, κ=0.56 for Backchannel and κ=0.5 for the Interruption/non-interruption distinction. More precise differences linked to cooperative or competitive behaviors lead to lower agreements. These results underline the importance of low-level features like TRP to derive a classification of turn changes that would be less subject to interpretation. The analysis of the presence of overlapping speech highlights the existence of interruptions without overlaps and smooth transitions with overlaps. These annotations are available at https://lium.univ-lemans.fr/corpus-allies/.

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Annotations for Exploring Food Tweets from Multiple Aspects
Matiss Rikters | Rinalds Vīksna | Edison Marrese-Taylor

This research builds upon the Latvian Twitter Eater Corpus (LTEC), which is focused on the narrow domain of tweets related to food, drinks, eating and drinking. LTEC has been collected for more than 12 years and reaching almost 3 million tweets with the basic information as well as extended automatically and manually annotated metadata. In this paper we supplement the LTEC with manually annotated subsets of evaluation data for machine translation, named entity recognition, timeline-balanced sentiment analysis, and text-image relation classification. We experiment with each of the data sets using baseline models and highlight future challenges for various modelling approaches.

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Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost
Oana Ignat | Longju Bai | Joan C. Nwatu | Rada Mihalcea

Current foundation models have shown impressive performance across various tasks. However, several studies have revealed that these models are not effective for everyone due to the imbalanced geographical and economic representation of the data used in the training process. Most of this data comes from Western countries, leading to poor results for underrepresented countries. To address this issue, more data needs to be collected from these countries, but the cost of annotation can be a significant bottleneck. In this paper, we propose methods to identify the data to be annotated to balance model performance and annotation costs. Our approach first involves finding the countries with images of topics (objects and actions) most visually distinct from those already in the training datasets used by current large vision-language foundation models. Next, we identify countries with higher visual similarity for these topics and show that using data from these countries to supplement the training data improves model performance and reduces annotation costs. The resulting lists of countries and corresponding topics are made available at https://github.com/MichiganNLP/visual_diversity_budget.

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AnnoTheia: A Semi-Automatic Annotation Toolkit for Audio-Visual Speech Technologies
José-M. Acosta-Triana | David Gimeno-Gómez | Carlos-D. Martínez-Hinarejos

More than 7,000 known languages are spoken around the world. However, due to the lack of annotated resources, only a small fraction of them are currently covered by speech technologies. Albeit self-supervised speech representations, recent massive speech corpora collections, as well as the organization of challenges, have alleviated this inequality, most studies are mainly benchmarked on English. This situation is aggravated when tasks involving both acoustic and visual speech modalities are addressed. In order to promote research on low-resource languages for audio-visual speech technologies, we present AnnoTheia, a semi-automatic annotation toolkit that detects when a person speaks on the scene and the corresponding transcription. In addition, to show the complete process of preparing AnnoTheia for a language of interest, we also describe the adaptation of a pre-trained model for active speaker detection to Spanish, using a database not initially conceived for this type of task. Prior evaluations show that the toolkit is able to speed up to four times the annotation process. The AnnoTheia toolkit, tutorials, and pre-trained models are available at https://github.com/joactr/AnnoTheia/.

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Announcing the Prague Discourse Treebank 3.0
Pavlína Synková | Jiří Mírovský | Lucie Poláková | Magdaléna Rysová

We present the Prague Discourse Treebank 3.0 – a new version of the annotation of discourse relations marked by primary and secondary discourse connectives in the data of the Prague Dependency Treebank. Compared to the previous version (PDiT 2.0), the version 3.0 comes with three types of major updates: (i) it brings a largely revised annotation of discourse relations: pragmatic relations have been thoroughly reworked, many inconsistencies across all discourse types have been fixed and previously unclear cases marked in annotators’ comments have been resolved, (ii) it achieves consistency with a Lexicon of Czech Discourse Connectives (CzeDLex), and (iii) it provides the data not only in its native format (Prague Markup Language, discourse relations annotated at the top of the dependency trees), but also in the Penn Discourse Treebank 3.0 format (plain text plus a stand-off discourse annotation) and sense taxonomy. PDiT 3.0 contains 21,662 discourse relations (plus 445 list relations) in 49 thousand sentences.

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A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models
Namu Park | Kevin Lybarger | Giridhar Kaushik Ramachandran | Spencer Lewis | Aashka Damani | Özlem Uzuner | Martin Gunn | Meliha Yetisgen

Medical imaging is critical to the diagnosis, surveillance, and treatment of many health conditions, including oncological, neurological, cardiovascular, and musculoskeletal disorders, among others. Radiologists interpret these complex, unstructured images and articulate their assessments through narrative reports that remain largely unstructured. This unstructured narrative must be converted into a structured semantic representation to facilitate secondary applications such as retrospective analyses or clinical decision support. Here, we introduce the Corpus of Annotated Medical Imaging Reports (CAMIR), which includes 609 annotated radiology reports from three imaging modality types: Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography-Computed Tomography. Reports were annotated using an event-based schema that captures clinical indications, lesions, and medical problems. Each event consists of a trigger and multiple arguments, and a majority of the argument types, including anatomy, normalize the spans to pre-defined concepts to facilitate secondary use. CAMIR uniquely combines a granular event structure and concept normalization. To extract CAMIR events, we explored two BERT (Bi-directional Encoder Representation from Transformers)-based architectures, including an existing architecture (mSpERT) that jointly extracts all event information and a multi-step approach (PL-Marker++) that we augmented for the CAMIR schema.

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A Novel Three-stage Framework for Few-shot Named Entity Recognition
Shengjie Ji | Fang Kong

Different from most existing tasks relying on abundant labeled data, Few-shot Named Entity Recognition (NER) aims to develop NER systems that are capable of learning from a small set of labeled samples and then generalizing well to new, unseen data.In this paper, with the intention of obtaining a model that can better adapt to new domains, we design a novel three-stage framework for Few-shot NER, including teacher span recognizer, student span recognizer and entity classifier.We first train a teacher span recognizer which is based on a global boundary matrix to obtain soft boundary labels.Then we leverage the soft boundary labels learned by the teacher model to assist in training the student span recognizer,which can smooth the training process of span recognizer.Finally, we adopt the traditional prototypical network as entity classifier and incorporate the idea of prompt learning to construct a more generalizable semantic space.Extensive experiments on various benchmarks demonstrate that our approach surpasses prior methods.

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AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments
Huadai Liu | Xu Wenqiang | Xuan Lin | Jingjing Huo | Hong Chen | Zhou Zhao

Argument mining aims to detect all possible argumentative components and identify their relationships automatically. As a thriving task in natural language processing, there has been a large amount of corpus for academic study and application development in this field. However, the research in this area is still constrained by the inherent limitations of existing datasets. Specifically, all the publicly available datasets are relatively small in scale, and few of them provide information from other modalities to facilitate the learning process. Moreover, the statements and expressions in these corpora are usually in a compact form, which restricts the generalization ability of models. To this end, we collect a novel dataset AntCritic to serve as a helpful complement to this area, which consists of about 10k free-form and visually-rich financial comments and supports both argument component detection and argument relation prediction tasks. Besides, to cope with the challenges brought by scenario expansion, we thoroughly explore the fine-grained relation prediction and structure reconstruction scheme and discuss the encoding mechanism for visual styles and layouts. On this basis, we design two simple but effective model architectures and conduct various experiments on this dataset to provide benchmark performances as a reference and verify the practicability of our proposed architecture. We release our data and code in this link, and this dataset follows CC BY-NC-ND 4.0 license.

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An Unsupervised Framework for Adaptive Context-aware Simplified-Traditional Chinese Character Conversion
Wei Li | Shutan Huang | Yanqiu Shao

Traditional Chinese character is an important carrier of Chinese culture, and is still actively used in many areas. Automatic conversion between traditional and simplified Chinese characters can help modern people understand traditional culture and facilitate communication among different regions. Previous conversion methods rely on rule-based mapping or shallow feature-based machine learning models, which struggle to convert simplified characters with different origins and constructing training data is costly. In this study, we propose an unsupervised adaptive context-aware conversion model that learns to convert between simplified and traditional Chinese characters under a denoising auto-encoder framework requiring no labeled data. Our model includes a Latent Generative Adversarial Encoder that transforms vectors to a latent space with generative adversarial network, which adds noise as an inevitable side effect, Based on which a Context-aware Semantic Reconstruction Decoder restores the original input while considering a broader range of context with a pretrained language model. Additionally, we propose to apply early exit mechanism during inference to reduce the computation complexity and improve the generalization ability. To test the effectiveness of our model, we construct a high quality test dataset with simplified-traditional Chinese character text pairs. Experiment results and extensive analysis demonstrate that our model outperforms strong unsupervised baselines and yields better conversion result for one-to-many cases.

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An Untold Story of Preprocessing Task Evaluation: An Alignment-based Joint Evaluation Approach
Eunkyul Leah Jo | Angela Yoonseo Park | Grace Tianjiao Zhang | Izia Xiaoxiao Wang | Junrui Wang | MingJia Mao | Jungyeul Park

A preprocessing task such as tokenization and sentence boundary detection (SBD) has commonly been considered as NLP challenges that have already been solved. This perception is due to their generally good performance and the presence of pre-tokenized data. However, it’s important to note that the low error rates of current methods are mainly specific to certain tasks, and rule-based tokenization can be difficult to use across different systems. Despite being subtle, these limitations are significant in the context of the NLP pipeline. In this paper, we introduce a novel evaluation algorithm for the preprocessing task, including both tokenization and SBD results. This algorithm aims to enhance the reliability of evaluations by reevaluating the counts of true positive cases for F1 measures in both preprocessing tasks jointly. It achieves this through an alignment-based approach inspired by sentence and word alignments used in machine translation. Our evaluation algorithm not only allows for precise counting of true positive tokens and sentence boundaries but also combines these two evaluation tasks into a single organized pipeline. To illustrate and clarify the intricacies of this calculation and integration, we provide detailed pseudo-code configurations for implementation. Additionally, we offer empirical evidence demonstrating how sentence and word alignment can improve evaluation reliability and present case studies to further support our approach.

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A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models
Chenyang Lyu | Zefeng Du | Jitao Xu | Yitao Duan | Minghao Wu | Teresa Lynn | Alham Fikri Aji | Derek F. Wong | Longyue Wang

Machine Translation (MT) has greatly advanced over the years due to the developments in deep neural networks. However, the emergence of Large Language Models (LLMs) like GPT-4 and ChatGPT is introducing a new phase in the MT domain. In this context, we believe that the future of MT is intricately tied to the capabilities of LLMs. These models not only offer vast linguistic understandings but also bring innovative methodologies, such as prompt-based techniques, that have the potential to further elevate MT. In this paper, we provide an overview of the significant enhancements in MT that are influenced by LLMs and advocate for their pivotal role in upcoming MT research and implementations. We highlight several new MT directions, emphasizing the benefits of LLMs in scenarios such as Long-Document Translation, Stylized Translation, and Interactive Translation. Additionally, we address the important concern of privacy in LLM-driven MT and suggest essential privacy-preserving strategies. By showcasing practical instances, we aim to demonstrate the advantages that LLMs offer, particularly in tasks like translating extended documents. We conclude by emphasizing the critical role of LLMs in guiding the future evolution of MT and offer a roadmap for future exploration in the sector.

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A Persona-Based Corpus in the Diabetes Self-Care Domain - Applying a Human-Centered Approach to a Low-Resource Context
Rossana Cunha | Thiago Castro Ferreira | Adriana Pagano | Fabio Alves

While Natural Language Processing (NLP) models have gained substantial attention, only in recent years has research opened new paths for tackling Human-Computer Design (HCD) from the perspective of natural language. We focus on developing a human-centered corpus, more specifically, a persona-based corpus in a particular healthcare domain (diabetes mellitus self-care). In order to follow an HCD approach, we created personas to model interpersonal interaction (expert and non-expert users) in that specific domain. We show that an HCD approach benefits language generation from different perspectives, from machines to humans - contributing with new directions for low-resource contexts (languages other than English and sensitive domains) where the need to promote effective communication is essential.

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APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning
Jiashuo Sun | Hang Zhang | Chen Lin | Xiangdong Su | Yeyun Gong | Jian Guo

Long-form numerical reasoning aims to generate a reasoning program to calculate the answer for a given question. Previous work followed a retriever-generator framework, where the retriever selects key facts from a long-form document, and the generator generates a reasoning program based on the retrieved facts. However, they treated all facts equally without considering the different contributions of facts with and without numerical information. Furthermore, they ignored program consistency, leading to the wrong punishment of programs that differed from the ground truth. In order to address these issues, we proposed APOLLO (An optimized training aPproach fOr Long-form numericaL reasOning), to improve long-form numerical reasoning. APOLLO includes a number-aware negative sampling strategy for the retriever to discriminate key numerical facts, and a consistency-based reinforcement learning with target program augmentation for the generator to ultimately increase the execution accuracy. Experimental results on the FinQA and ConvFinQA leaderboards verify the effectiveness of our proposed methods, achieving the new state-of-the-art.

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Applying Transfer Learning to German Metaphor Prediction
Maria Berger | Sebastian Michael Reimann | Nieke Marie Kiwitt

This paper presents results in transfer-learning metaphor recognition in German. Starting from an English language corpus annotated for metaphor at the sentence level, and its machine-translation to German, we annotate 1000 sentences of the German part to use it as a Gold standard for two different metaphor prediction setups: i) a sequence labeling set-up (on the token-level), and ii) a classification (based on sentences) setup. We test two transfer leaning approaches: i) a group of transformer models, and ii) a technique that utilizes bilingual embeddings together with an RNN classifier. We find out that the transformer models do moderately in a zero-shot scenario (up to 61% F1 for classification) and the embeddings approaches do not even beat the guessing baseline (36% F1 for classification). We use our Gold data to fine-tune the classification tasks on target-language data achieving up to 90% F1 with both, the multilingual BERT and the bilingual embeddings. We also publish the annotated bilingual corpus.

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Appraisal Framework for Clinical Empathy: A Novel Application to Breaking Bad News Conversations
Allison Claire Lahnala | Béla Neuendorf | Alexander Thomin | Charles Welch | Tina Stibane | Lucie Flek

Empathy is essential in healthcare communication. We introduce an annotation approach that draws on well-established frameworks for clinical empathy and breaking bad news (BBN) conversations for considering the interactive dynamics of discourse relations. We construct Empathy in BBNs, a span-relation task dataset of simulated BBN conversations in German, using our annotation scheme, in collaboration with a large medical school to support research on educational tools for medical didactics. The annotation is based on 1) Pounds (2011)’s appraisal framework for clinical empathy, which is grounded in systemic functional linguistics, and 2) the SPIKES protocol for breaking bad news (Baile et al., 2000), commonly taught in medical didactics training. This approach presents novel opportunities to study clinical empathic behavior and enables the training of models to detect causal relations involving empathy, a highly desirable feature of systems that can provide feedback to medical professionals in training. We present illustrative examples, discuss applications of the annotation scheme, and insights we can draw from the framework.

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Approaches and Challenges for Resolving Different Representations of Fictional Characters for Chinese Novels
Li Song | Ying Liu

Due to the huge scale of literary works, automatic text analysis technologies are urgently needed for literary studies such as Digital Humanities. However, the domain-generality of existing NLP technologies limits their effectiveness on in-depth literary studies. It is valuable to explore how to adapt NLP technologies to the literary-specific tasks. Fictional characters are the most essential elements of a novel, and thus crucial to understanding the content of novels. The prerequisite of collecting a character’s information is to resolve its different representations. It is a specific problem of anaphora resolution which is a classical and open-domain NLP task. We adapt a state-of-the-art anaphora resolution model to resolve character representations in Chinese novels by making some modifications, and train a widely used BERT fine-tuned model for speaker extraction as assistance. We also analyze the challenges and potential solutions for character-resolution in Chinese novels according to the resolution results on a specific Chinese novel.

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A Preliminary Study of ChatGPT for Spanish E2R Text Adaptation
Margot Madina | Itziar Gonzalez-Dios | Melanie Siegel

The process of adapting and creating Easy-to-Read (E2R) texts is very expensive and time-consuming. Due to the success of Large Language Models (LLMs) such as ChatGPT and their ability to generate written language, it is likely to think that such models can help in the adaptation or creation of text in E2R. In this paper, we explore the concept of E2R, its underlying principles and applications, and provides a preliminary study on the usefulness of ChatGPT-4 for E2R text adaptation. We focus on the Spanish language and its E2R variant, Lectura Fácil (LF). We consider a range of prompts that can be used and the differences in output that this produces. We then carry out a three-folded evaluation on 10 texts adapted by ChatGPT-4: (1) an automated evaluation to check values related to the readability of texts, (2) a checklist-based manual evaluation (for which we also propose three new capabilities) and (3) a users’ evaluation with people with cognitive disabilities. We show that it is difficult to choose the best prompt to make ChatGPT-4 adapt texts to LF. Furthermore, the generated output does not follow the E2R text rules, so it is often not suitable for the target audience.

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A Quantum-Inspired Matching Network with Linguistic Theories for Metaphor Detection
Wenbo Qiao | Peng Zhang | ZengLai Ma

Enabling machines with the capability to recognize and comprehend metaphors is a crucial step toward achieving artificial intelligence. In linguistic theories, metaphor can be identified through Metaphor Identification Procedure (MIP) or Selectional Preference Violation (SPV), both of which are typically considered as matching tasks in the field of natural language processing. However, the implementation of MIP poses a challenge due to the semantic uncertainty and ambiguity of literal meanings of words. Simultaneously, SPV often struggles to recognize conventional metaphors. Inspired by Quantum Language Model (QLM) for modeling semantic uncertainty and fine-grained feature matching, we propose a quantum-inspired matching network for metaphor detection. Specifically, we use the density matrix to explicitly characterize the literal meanings of the target word for MIP, in order to model the uncertainty and ambiguity of the literal meanings of words. This can make SPV effective even in the face of conventional metaphors. MIP and SPV are then achieved by fine-grained feature matching. The results of the experiment finally demonstrated our approach has strong competitiveness.

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Arabic Diacritization Using Morphologically Informed Character-Level Model
Muhammad Morsy Elmallah | Mahmoud Reda | Kareem Darwish | Abdelrahman El-Sheikh | Ashraf Hatim Elneima | Murtadha Aljubran | Nouf Alsaeed | Reem Mohammed | Mohamed Al-Badrashiny

Arabic diacritic recovery i.e. diacritization is necessary for proper vocalization and an enabler for downstream applications such as language learning and text to speech. Diacritics come in two varieties, namely: core-word diacritics and case endings. In this paper we introduce a highly effective morphologically informed character-level model that can recover both types of diacritics simultaneously. The model uses a Recurrent Neural Network (RNN) based architecture that takes in text as a sequence of characters, with markers for morphological segmentation, and outputs a sequence of diacritics. We also introduce a character-based morphological segmentation model that we train for Modern Standard Arabic (MSA) and dialectal Arabic. We demonstrate the efficacy of our diacritization model on Classical Arabic, MSA, and two dialectal (Moroccan and Tunisian) texts. We achieve the lowest reported word-level diacritization error rate for MSA (3.4%), match the best results for Classical Arabic (5.4%), and report competitive results for dialectal Arabic.

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Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding
Zhiyu Fang | Jingyan Qin | Xiaobin Zhu | Chun Yang | Xu-Cheng Yin

Distinguished from traditional knowledge graphs (KGs), temporal knowledge graphs (TKGs) must explore and reason over temporally evolving facts adequately. However, existing TKG approaches still face two main challenges, i.e., the limited capability to model arbitrary timestamps continuously and the lack of rich inference patterns under temporal constraints. In this paper, we propose an innovative TKGE method (PTBox) via polynomial decomposition-based temporal representation and box embedding-based entity representation to tackle the above-mentioned problems. Specifically, we decompose time information by polynomials and then enhance the model’s capability to represent arbitrary timestamps flexibly by incorporating the learnable temporal basis tensor. In addition, we model every entity as a hyperrectangle box and define each relation as a transformation on the head and tail entity boxes. The entity boxes can capture complex geometric structures and learn robust representations, improving the model’s inductive capability for rich inference patterns. Theoretically, our PTBox can encode arbitrary time information or even unseen timestamps while capturing rich inference patterns and higher-arity relations of the knowledge base. Extensive experiments on real-world datasets demonstrate the effectiveness of our method.

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ARBRES Kenstur: A Breton-French Parallel Corpus Rooted in Field Linguistics
Loïc Grobol | Mélanie Jouitteau

ARBRES is an ongoing project of open science implemented as a platform (“wikigrammar”) documenting both the Breton language itself and the state of research and engineering work in linguistics and NLP. Along its nearly 15 years of operation, it has aggregated a wealth of linguistic data in the form of interlinear glosses with translations illustrating lexical items, grammatical features, dialectal variations... While these glosses were primarily meant for human consumption, their volume and the regular format imposed by the wiki engine used for the website also make them suitable for machine processing. ARBRES Kenstur is a new parallel corpus derived from the glosses in ARBRES, including about 5k phrases and sentences in Breton along with translations in standard French. The nature of the original data — sourced from field linguistic inquiries meant to document the structure of Breton — leads to a resource that is mechanically more concerned with the internal variations of the language and rare phenomena than typical parallel corpora. Preliminaries experiments in using this corpus show that it can help improve machine translation for Breton, demonstrating that sourcing data from field linguistic documentation can be a way to help provide NLP tools for minority and low-resource languages.

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A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder
Kedi Chen | Jie Zhou | Qin Chen | Shunyu Liu | Liang He

Information extraction (IE) aims to extract complex structured information from the text. Numerous datasets have been constructed for various IE tasks, leading to time-consuming and labor-intensive data annotations. Nevertheless, most prevailing methods focus on training task-specific models, while the common knowledge among different IE tasks is not explicitly modeled. Moreover, the same phrase may have inconsistent labels in different tasks, which poses a big challenge for knowledge transfer using a unified model. In this study, we propose a regularization-based transfer learning method for IE (TIE) via an instructed graph decoder. Specifically, we first construct an instruction pool for datasets from all well-known IE tasks, and then present an instructed graph decoder, which decodes various complex structures into a graph uniformly based on corresponding instructions. In this way, the common knowledge shared with existing datasets can be learned and transferred to a new dataset with new labels. Furthermore, to alleviate the label inconsistency problem among various IE tasks, we introduce a task-specific regularization strategy, which does not update the gradients of two tasks with ‘opposite direction’. We conduct extensive experiments on 12 datasets spanning four IE tasks, and the results demonstrate the great advantages of our proposed method.

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A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data
Hongxiao Zhang | Mingtong Liu | Chunyou Li | Yufeng Chen | Jinan Xu | Ming Zhou

Due to the lack of parallel data, the mainstream fine-tuning-based domain adaptation methods have the overfitting problem in the translation of low-resource domains, and it is difficult for the model to learn the in-domain generalization knowledge. To address the above issue, in this work, we propose a novel Reinforcement Learning Domain Adaptation method for Neural Machine Translation (RLDA-NMT) in the low-resource domain. RLDA-NMT utilizes in-domain source monolingual data to make up for the lack of parallel data, and reinforces domain features learning to make the translation model learn the domain-specific knowledge more fully. Specifically, we first train a ranking-based model with a small-scale in-domain parallel corpus, and then adopt it as the reward model to select higher-quality generated translations for reinforcement when fine-tuning pre-trained NMT model using in-domain source monolingual data. We conduct experiments on Education, Laws, Thesis, and Patent domains of Chinese⇔English translation tasks. Experimental results demonstrate that RLDA-NMT can alleviate overfitting and reinforce the NMT model to learn domain-specific knowledge. Additionally, the results also show that RLDA-NMT and back-translation (BT) are nicely complementary to each other, where combining RLDA-NMT with BT can further improve translation quality.

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Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning
Viktor Moskvoretskii | Alexander Panchenko | Irina Nikishina

Recent studies on LLMs do not pay enough attention to linguistic and lexical semantic tasks, such as taxonomy learning. In this paper, we explore the capacities of Large Language Models featuring LLaMA-2 and Mistral for several Taxonomy-related tasks. We introduce a new methodology and algorithm for data collection via stochastic graph traversal leading to controllable data collection. Collected cases provide the ability to form nearly any type of graph operation. We test the collected dataset for learning taxonomy structure based on English WordNet and compare different input templates for fine-tuning LLMs. Moreover, we apply the fine-tuned models on such datasets on the downstream tasks achieving state-of-the-art results on the TexEval-2 dataset.

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Are Text Classifiers Xenophobic? A Country-Oriented Bias Detection Method with Least Confounding Variables
Valentin Barriere | Sebastian Cifuentes

Classical bias detection methods used in Machine Learning are themselves biased because of the different confounding variables implied in the assessment of the initial biases. First they are using templates that are syntactically simple and distant from the target data on which the model will deployed. Second, current methods are assessing biases in pre-trained language models or in dataset, but not directly on the fine-tuned classifier that can actually produce harms. We propose a simple method to detect the biases of a specific fine-tuned classifier on any type of unlabeled data. The idea is to study the classifier behavior by creating counterfactual examples directly on the target data distribution and quantify the amount of changes. In this work, we focus on named entity perturbations by applying a Named Entity Recognition on target-domain data and modifying them accordingly to most common names or location of a target group (gender and country), and this for several morphosynctactically different languages spoken in relation with the countries of the target groups. We used our method on two models available open-source that are likely to be deployed by industry, and on two tasks and domains. We first assess the bias of a multilingual sentiment analysis model trained over multiple-languages tweets and available open-source, and then a multilingual stance recognition model trained over several languages and assessed over English language. Finally we propose to link the perplexity of each example with the bias of the model, by looking at the change in label distribution with respect to the language of the target group. Our work offers a fine-grained analysis of the interactions between names and languages, revealing significant biases in multilingual models.

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Argument Quality Assessment in the Age of Instruction-Following Large Language Models
Henning Wachsmuth | Gabriella Lapesa | Elena Cabrio | Anne Lauscher | Joonsuk Park | Eva Maria Vecchi | Serena Villata | Timon Ziegenbein

The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like. A critical task in any such application is the assessment of an argument’s quality - but it is also particularly challenging. In this position paper, we start from a brief survey of argument quality research, where we identify the diversity of quality notions and the subjectiveness of their perception as the main hurdles towards substantial progress on argument quality assessment. We argue that the capabilities of instruction-following large language models (LLMs) to leverage knowledge across contexts enable a much more reliable assessment. Rather than just fine-tuning LLMs towards leaderboard chasing on assessment tasks, they need to be instructed systematically with argumentation theories and scenarios as well as with ways to solve argument-related problems. We discuss the real-world opportunities and ethical issues emerging thereby.

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Article Classification with Graph Neural Networks and Multigraphs
Khang Ly | Yury Kashnitsky | Savvas Chamezopoulos | Valeria Krzhizhanovskaya

Classifying research output into context-specific label taxonomies is a challenging and relevant downstream task, given the volume of existing and newly published articles. We propose a method to enhance the performance of article classification by enriching simple Graph Neural Network (GNN) pipelines with multi-graph representations that simultaneously encode multiple signals of article relatedness, e.g. references, co-authorship, shared publication source, shared subject headings, as distinct edge types. Fully supervised transductive node classification experiments are conducted on the Open Graph Benchmark OGBN-arXiv dataset and the PubMed diabetes dataset, augmented with additional metadata from Microsoft Academic Graph and PubMed Central, respectively. The results demonstrate that multi-graphs consistently improve the performance of a variety of GNN models compared to the default graphs. When deployed with SOTA textual node embedding methods, the transformed multi-graphs enable simple and shallow 2-layer GNN pipelines to achieve results on par with more complex architectures.

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ART: The Alternating Reading Task Corpus for Speech Entrainment and Imitation
Zheng Byron Yuan | Dorina de Jong | Ruitao Feng | Štefan Beňuš | Noël Nguyen | Róbert Sabo | Luciano Fadiga | Alessandro D’Ausilio

We introduce the Alternating Reading Task (ART) Corpus, a collection of dyadic sentence reading for studying the entrainment and imitation behaviour in speech communication. The ART corpus features three experimental conditions - solo reading, alternating reading, and deliberate imitation - as well as three subcorpora encompassing French-, Italian-, and Slovak-accented English. This design allows systematic investigation of speech entrainment in a controlled and less spontaneous setting. Alongside detailed transcriptions, it includes English proficiency scores, demographics, and in-experiment questionnaires for probing linguistic, personal and interpersonal influences on entrainment. Our presentation covers its design, collection, annotation processes, initial analysis, and future research prospects.

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A Self-verified Method for Exploring Simile Knowledge from Pre-trained Language Models
Longxuan Ma | Changxin Ke | Shuhan Zhou | Churui Sun | Wei-Nan Zhang | Ting Liu

Simile tasks are challenging in natural language processing (NLP) because models require adequate world knowledge to produce predictions. In recent years, pre-trained language models (PLMs) have succeeded in NLP since they learn generic knowledge from a large corpus. The knowledge embedded in PLMs can be used for different kinds of Simile tasks. However, previous work usually explored one type of simile knowledge for a specific simile task, how to fully utilize different types of knowledge embedded in the PLMs requires further exploration. This paper proposes a self-verified method for exploring simile knowledge from PLMs, which allows the PLMs to leverage one type of simile knowledge to self-validate another. To this end, we first enhance PLMs with a novel multi-level simile recognition (MLSR) task that trains PLMs to evaluate the quality of similes. Then the PLMs leverage this evaluation score to assist the simile interpretation and generation tasks. In this way, we connect different types of simile knowledge in PLMs and make better use of them. Experiments on different pre-trained models and multiple publicly available datasets show that our method works for different kinds of PLMs and can explore more accurate simile knowledge for PLMs. Our code/data will be released on GitHub.

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A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction
Jian Zhang | Changlin Yang | Haiping Zhu | Qika Lin | Fangzhi Xu | Jun Liu

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions; b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensemble graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance.

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ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling
Omama Hamad | Khaled Shaban | Ali Hamdi

Effective feature representations play a critical role in enhancing the performance of text generation models that rely on deep neural networks. However, current approaches suffer from several drawbacks, such as the inability to capture the deep semantics of language and sensitivity to minor input variations, resulting in significant changes in the generated text. In this paper, we present a novel solution to these challenges by employing a mixture of experts, multiple encoders, to offer distinct perspectives on the emotional state of the user’s utterance while simultaneously enhancing performance. We propose an end-to-end model architecture called ASEM that performs emotion analysis on top of sentiment analysis for open-domain chatbots, enabling the generation of empathetic responses that are fluent and relevant. In contrast to traditional attention mechanisms, the proposed model employs a specialized attention strategy that uniquely zeroes in on sentiment and emotion nuances within the user’s utterance. This ensures the generation of context-rich representations tailored to the underlying emotional tone and sentiment intricacies of the text. Our approach outperforms existing methods for generating empathetic embeddings, providing empathetic and diverse responses. The performance of our proposed model significantly exceeds that of existing models, enhancing emotion detection accuracy by 6.2% and lexical diversity by 1.4%. ASEM code is released at https://github.com/MIRAH-Official/Empathetic-Chatbot-ASEM.git

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A Single Linear Layer Yields Task-Adapted Low-Rank Matrices
Hwichan Kim | Shota Sasaki | Sho Hoshino | Ukyo Honda

Low-Rank Adaptation (LoRA) is a widely used Parameter-Efficient Fine-Tuning (PEFT) method that updates an initial weight matrix W0 with a delta matrix 𝛥 W consisted by two low-rank matrices A and B. A previous study suggested that there is correlation between W0 and 𝛥 W. In this study, we aim to delve deeper into relationships between W0 and low-rank matrices A and B to further comprehend the behavior of LoRA. In particular, we analyze a conversion matrix that transform W0 into low-rank matrices, which encapsulates information about the relationships. Our analysis reveals that the conversion matrices are similar across each layer. Inspired by these findings, we hypothesize that a single linear layer, which takes each layer’s W0 as input, can yield task-adapted low-rank matrices. To confirm this hypothesis, we devise a method named Conditionally Parameterized LoRA (CondLoRA) that updates initial weight matrices with low-rank matrices derived from a single linear layer. Our empirical results show that CondLoRA maintains a performance on par with LoRA, despite the fact that the trainable parameters of CondLoRA are fewer than those of LoRA. Therefore, we conclude that “a single linear layer yields task-adapted low-rank matrices.” The code used in our experiments is available at https://github.com/CyberAgentAILab/CondLoRA.

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Asking and Answering Questions to Extract Event-Argument Structures
Md Nayem Uddin | Enfa Rose George | Eduardo Blanco | Steven R. Corman

This paper presents a question-answering approach to extract document-level event-argument structures. We automatically ask and answer questions for each argument type an event may have. Questions are generated using manually defined templates and generative transformers. Template-based questions are generated using predefined role-specific wh-words and event triggers from the context document. Transformer-based questions are generated using large language models trained to formulate questions based on a passage and the expected answer. Additionally, we develop novel data augmentation strategies specialized in inter-sentential event-argument relations. We use a simple span-swapping technique, coreference resolution, and large language models to augment the training instances. Our approach enables transfer learning without any corpora-specific modifications and yields competitive results with the RAMS dataset. It outperforms previous work, and it is especially beneficial to extract arguments that appear in different sentences than the event trigger. We also present detailed quantitative and qualitative analyses shedding light on the most common errors made by our best model.

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AssameseBackTranslit: Back Transliteration of Romanized Assamese Social Media Text
Hemanta Baruah | Sanasam Ranbir Singh | Priyankoo Sarmah

This paper presents a novel back transliteration dataset capturing native language text originally composed in the Roman/Latin script, harvested from popular social media platforms, along with its corresponding representation in the native Assamese script. Assamese, categorized as a low-resource language within the Indo-Aryan language family, predominantly spoken in the north-east Indian state of Assam, faces a scarcity of linguistic resources. The dataset comprises a total of 60,312 Roman-native parallel transliterated sentences. This paper diverges from conventional forward transliteration datasets consisting mainly of named entities and technical terms, instead presenting a novel transliteration dataset cultivated from three prominent social media platforms, Facebook, Twitter(currently X), and YouTube, in the backward transliteration direction. The paper offers a comprehensive examination of ten state-of-the-art word-level transliteration models within the context of this dataset, encompassing transliteration evaluation benchmarks, extensive performance assessments, and a discussion of the unique chal- lenges encountered during the processing of transliterated social media content. Our approach involves the initial use of two statistical transliteration models, followed by the training of two state-of-the-art neural network-based transliteration models, evaluation of three publicly available pre-trained models, and ultimately fine-tuning one existing state-of-the-art multilingual transliteration model along with two pre-trained large language models using the collected datasets. Notably, the Neural Transformer model outperforms all other baseline transliteration models, achieving the lowest Word Error Rate (WER) and Character Error Rate (CER), and the highest BLEU (up to 4 gram) score of 55.05, 19.44, and 69.15, respectively.

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Assessing Online Writing Feedback Resources: Generative AI vs. Good Samaritans
Shabnam Behzad | Omid Kashefi | Swapna Somasundaran

Providing constructive feedback on student essays is a critical factor in improving educational results; however, it presents notable difficulties and may demand substantial time investments, especially when aiming to deliver individualized and informative guidance. This study undertakes a comparative analysis of two readily available online resources for students seeking to hone their skills in essay writing for English proficiency tests: 1) essayforum.com, a widely used platform where students can submit their essays and receive feedback from volunteer educators at no cost, and 2) Large Language Models (LLMs) such as ChatGPT. By contrasting the feedback obtained from these two resources, we posit that they can mutually reinforce each other and are more helpful if employed in conjunction when seeking no-cost online assistance. The findings of this research shed light on the challenges of providing personalized feedback and highlight the potential of AI in advancing the field of automated essay evaluation.

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Assessing the Capabilities of Large Language Models in Coreference: An Evaluation
Yujian Gan | Massimo Poesio | Juntao Yu

This paper offers a nuanced examination of the role Large Language Models (LLMs) play in coreference resolution, aimed at guiding the future direction in the era of LLMs. We carried out both manual and automatic analyses of different LLMs’ abilities, employing different prompts to examine the performance of different LLMs, obtaining a comprehensive view of their strengths and weaknesses. We found that LLMs show exceptional ability in understanding coreference. However, harnessing this ability to achieve state of the art results on traditional datasets and benchmarks isn’t straightforward. Given these findings, we propose that future efforts should: (1) Improve the scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. (2) Enhance the fine-grained language understanding capabilities of LLMs.

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Assessing the Efficacy of Grammar Error Correction: A Human Evaluation Approach in the Japanese Context
Qiao Wang | Zheng Yuan

In this study, we evaluated the performance of the state-of-the-art sequence tagging grammar error detection and correction model (SeqTagger) using Japanese university students’ writing samples. With an automatic annotation toolkit, ERRANT, we first evaluated SeqTagger’s performance on error correction with human expert correction as the benchmark. Then a human-annotated approach was adopted to evaluate Seqtagger’s performance in error detection using a subset of the writing dataset. Results indicated a precision of 63.66% and a recall of 20.19% for error correction in the full dataset. For the subset, after manual exclusion of irrelevant errors such as semantic and mechanical ones, the model shows an adjusted precision of 97.98% and an adjusted recall of 42.98% for error detection, indicating the model’s high accuracy but also its conservativeness. Thematic analysis on errors undetected by the model revealed that determiners and articles, especially the latter, were predominant. Specifically, in terms of context-independent errors, the model occasionally overlooked basic ones and faced challenges with overly erroneous or complex structures. Meanwhile, context-dependent errors, notably those related to tense and noun number, as well as those possibly influenced by the students’ first language (L1), remained particularly challenging.

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A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition
Wenjie Xu | Yidan Chen | Jianquan Ouyang

Few-shot named entity recognition (NER) is a challenging task that aims to recognize new named entities with only a limited amount of labeled examples. In this paper, we introduce SSF, which is a streamlined span-based factorization method that addresses the problem of few-shot NER. Our approach formulates few-shot NER as a span-level alignment problem between query and support instances. To achieve this goal, SSF decomposes the span-level alignment problem into several refined span-level procedures. The proposed approach encompasses several key modules such as the Span Boosting Module, Span Prototypical Module, Span Alignment Module, and Span Optimization Module. Our experimental results demonstrate a significant improvement over the previous state-of-the-art performance. Specifically, compared to previous methods, our proposed approach achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on the SNIPS dataset. Moreover, our approach has surpassed the latest state-of-the-art performance on both datasets.

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A Study on How Attention Scores in the BERT Model Are Aware of Lexical Categories in Syntactic and Semantic Tasks on the GLUE Benchmark
Dongjun Jang | Sungjoo Byun | Hyopil Shin

This study examines whether the attention scores between tokens in the BERT model significantly vary based on lexical categories during the fine-tuning process for downstream tasks. Drawing inspiration from the notion that in human language processing, syntactic and semantic information is parsed differently, we categorize tokens in sentences according to their lexical categories and focus on changes in attention scores among these categories. Our hypothesis posits that in downstream tasks that prioritize semantic information, attention scores centered on content words are enhanced, while in cases emphasizing syntactic information, attention scores centered on function words are intensified. Through experimentation conducted on six tasks from the GLUE benchmark dataset, we substantiate our hypothesis regarding the fine-tuning process. Furthermore, our additional investigations reveal the presence of BERT layers that consistently assign more bias to specific lexical categories, irrespective of the task, highlighting the existence of task-agnostic lexical category preferences.

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A Survey on Natural Language Processing for Programming
Qingfu Zhu | Xianzhen Luo | Fang Liu | Cuiyun Gao | Wanxiang Che

Natural language processing for programming aims to use NLP techniques to assist programming. It is increasingly prevalent for its effectiveness in improving productivity. Distinct from natural language, a programming language is highly structured and functional. Constructing a structure-based representation and a functionality-oriented algorithm is at the heart of program understanding and generation. In this paper, we conduct a systematic review covering tasks, datasets, evaluation methods, techniques, and models from the perspective of the structure-based and functionality-oriented property, aiming to understand the role of the two properties in each component. Based on the analysis, we illustrate unexplored areas and suggest potential directions for future work.

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A Tool for Determining Distances and Overlaps between Multimodal Annotations
Camila Antonio Barros | Jorge Francisco Ciprián-Sánchez | Saulo Mendes Santos

Comparing annotations is a constant and necessary step in corpus analysis. Although the nature of these annotations is normally research-specific, the tools used for this purpose do not have to be. Here, we present a tool for extracting and comparing annotations from ELAN, despite their idiosyncrasies. The intention behind this tool is to provide a handy way to analyze ELAN annotated files, by comparing tiers to a reference unit. Using the presented tool, it is possible to see how tiers overlap (even if they are of symbolic type), to which ratio, and the displacement regarding a reference unit. We present an example of multimodal corpus analysis, regarding the coordination between speech and gesture units based on a pragmatic reference. We argue that looking into overlap ratios can be more informative of the association between speech and gestures, and that considering a time buffer between speech and gestural events can be misleading.

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A Treebank of Asia Minor Greek
Eleni Vligouridou | Inessa Iliadou | Çağrı Çöltekin

Asia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and culture that face a dire struggle for preservation due to declining speaker base and scarce linguistic resources. To address this need, we introduce a Universal Dependencies treebank of Pharasiot Greek, one of the severly endangerd AMG dialects. The present treebank is fully manually annotated and currently consists of 350 sentences from six fairy tales in Pharasiot dialect. Besides describing the treebank and the annotation process, we provide and discuss interesting phenomena we observed in the treebank. Most phenomena we discuss are related to contact-induced linguistic changes that these dialects are well known for. Beyond linguistic inquiry, like other treebanks for truly low-resource languages, the AMG treebank we present offers potentials for diverse applications, such as language preservation and revitalization, as well as NLP tools that have to be developed with scarce resources.

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A Trusted Multi-View Evidential Fusion Framework for Commonsense Reasoning
Shuo Yang

While deep learning models are powerful, they have limitations in tasks that require commonsense reasoning, as these tasks often involve interpreting information that may not be directly available in the input. Providing evidence has been proven to significantly enhance performance in commonsense reasoning tasks. However, there are various perspectives on evidence, including natural language explanations generated by pre-trained language models, facts derived from world knowledge like text corpora and knowledge bases, and rationales extracted from the input context. Hence, it is crucial to determine how to estimate the confidence degree of different evidence and how to combine them reliably. To address these challenges, this study proposes a trusted multi-view evidential fusion framework for reliable commonsense reasoning tasks that dynamically assesses the confidence of evidence and combines different views of evidence in a trustworthy manner. The proposed method is applied to three commonsense question-answering benchmarks, demonstrating that this approach can effectively reason with multi-view evidence and can compete with state-of-the-art performance.

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Attack Named Entity Recognition by Entity Boundary Interference
Yifei Yang | Hongqiu Wu | Hai Zhao

Named Entity Recognition (NER) is a cornerstone natural language processing task while its robustness has been given little attention. This paper rethinks the principles of the conventional text attack, as they can easily violate the label consistency between the original and adversarial NER samples. This is due to the fine-grained nature of NER, as even minor word changes in the sentence can result in the emergence or mutation of any entity, producing invalid adversarial samples. To this end, we propose a novel one-word modification NER attack based on a key insight, NER models are always vulnerable to the boundary position of an entity to make their decision. We thus strategically insert a new boundary into the sentence and trigger the victim model to make a wrong recognition either on this boundary word or on other words in the sentence. We call this attack Virtual Boundary Attack (ViBA), which is shown to be remarkably effective when attacking both English and Chinese models with a 70%-90% attack success rate on state-of-the-art language models, and also significantly faster than previous methods.

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At the Crossroad of Cuneiform and NLP: Challenges for Fine-grained Part-of-speech Tagging
Gustav Ryberg Smidt | Els Lefever | Katrien de Graef

The study of ancient Middle Eastern cultures is dominated by the vast number of cuneiform texts. Multiple languages and language families were expressed in cuneiform. The most dominant language written in cuneiform is the Semitic Akkadian, which is the focus of this paper. We are specifically focusing on letters written in the dialect used in modern-day Baghdad and south towards the Persian Gulf during the Old Babylonian period (c. 2000-1600 B.C.E.). The Akkadian language was rediscovered in the 19th century and is now being scrutinised by Natural Language Processing (NLP) methods. However, existing Akkadian text publications are not always suitable for digital editions. We therefore risk applying NLP methods onto renderings of Akkadian unfit for the purpose. In this paper we want to investigate the input material and try to initiate a discussion about best-practices in the crossroad where NLP meets cuneiform studies. Specifically, we want to question the use of pre-trained embeddings, sentence segmentation and the type of cuneiform input used to fine-tune language models for the task of fine-grained part-of-speech tagging. We examine the issues by theoretical and practical approaches in a way that we hope spurs discussions that are relevant for automatic processing of other ancient languages.

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A Tulu Resource for Machine Translation
Manu Narayanan | Noëmi Aepli

We present the first parallel dataset for English–Tulu translation. Tulu, classified within the South Dravidian linguistic family branch, is predominantly spoken by approximately 2.5 million individuals in southwestern India. Our dataset is constructed by integrating human translations into the multilingual machine translation resource FLORES-200. Furthermore, we use this dataset for evaluation purposes in developing our English–Tulu machine translation model. For the model’s training, we leverage resources available for related South Dravidian languages. We adopt a transfer learning approach that exploits similarities between high-resource and low-resource languages. This method enables the training of a machine translation system even in the absence of parallel data between the source and target language, thereby overcoming a significant obstacle in machine translation development for low-resource languages. Our English–Tulu system, trained without using parallel English–Tulu data, outperforms Google Translate by 19 BLEU points (in September 2023). The dataset and code are available here: https://github.com/manunarayanan/Tulu-NMT.

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A Two-Stage Framework with Self-Supervised Distillation for Cross-Domain Text Classification
Yunlong Feng | Bohan Li | Libo Qin | Xiao Xu | Wanxiang Che

Cross-domain text classification is a crucial task as it enables models to adapt to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source domain(s) and unlabeled data from the target domain. To this end, previous work focuses on either extracting domain-invariant features or task-agnostic features, ignoring domain-aware features that may be present in the target domain and could be useful for the downstream task. In this paper, we propose a two-stage framework for cross-domain text classification. In the first stage, we finetune the model with mask language modeling (MLM) and labeled data from the source domain. In the second stage, we further fine-tune the model with self-supervised distillation (SSD) and unlabeled data from the target domain. We evaluate its performance on a public cross-domain text classification benchmark and the experiment results show that our method achieves new state-of-the-art results for both single-source domain adaptations (94.17% +1.03%) and multi-source domain adaptations (95.09% +1.34%).

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A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
Guanhua Chen | Yutong Yao | Derek F. Wong | Lidia S. Chao

Multi-intent natural language understanding (NLU) presents a formidable challenge due to the model confusion arising from multiple intents within a single utterance. While previous works train the model contrastively to increase the margin between different multi-intent labels, they are less suited to the nuances of multi-intent NLU. They ignore the rich information between the shared intents, which is beneficial to constructing a better embedding space, especially in low-data scenarios. We introduce a two-stage Prediction-Aware Contrastive Learning (PACL) framework for multi-intent NLU to harness this valuable knowledge. Our approach capitalizes on shared intent information by integrating word-level pre-training and prediction-aware contrastive fine-tuning. We construct a pre-training dataset using a word-level data augmentation strategy. Subsequently, our framework dynamically assigns roles to instances during contrastive fine-tuning while introducing a prediction-aware contrastive loss to maximize the impact of contrastive learning. We present experimental results and empirical analysis conducted on three widely used datasets, demonstrating that our method surpasses the performance of three prominent baselines on both low-data and full-data scenarios.

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A Typology of Errors for User Utterances in Chatbots
Anu Singh | Esme Manandise

This paper discusses the challenges non-prescriptive language uses in chatbot communication create for Semantic Parsing (SP). To help SP developers improve their systems, we propose a flexible error typology based on an analysis of a sample of non-prescriptive language uses mined from a domain-specific chatbot logs. This typology is not tied to any specific language model. We also present a framework for automatically mapping these errors to the typology. Finally, we show how our framework can help evaluate SP systems from a linguistic robustness perspective. Our framework can be expanded to include new classes of errors across different domains and user demographics.

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Audiocite.net : A Large Spoken Read Dataset in French
Soline Felice | Solene Virginie Evain | Solange Rossato | François Portet

The advent of self-supervised learning (SSL) in speech processing has allowed the use of large unlabeled datasets to learn pre-trained models, serving as powerful encoders for various downstream tasks. However, the application of these SSL methods to languages such as French has proved difficult due to the scarcity of large French speech datasets. To advance the emergence of pre-trained models for French speech, we present the Audiocite.net corpus composed of 6,682 hours of recordings from 130 readers. This corpus is composed of audiobooks from the audiocite.net website, shared by 130 readers. In addition to describing the creation process and final statistics, we also show how this dataset impacted the models of LeBenchmark project in its 14k version for speech processing downstream tasks.

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AuRoRA: A One-for-all Platform for Augmented Reasoning and Refining with Task-Adaptive Chain-of-Thought Prompting
Anni Zou | Zhuosheng Zhang | Hai Zhao

Large language models (LLMs) empowered by chain-of-thought (CoT) prompting have yielded remarkable prowess in reasoning tasks. Nevertheless, current methods predominantly lean on handcrafted or task-specific demonstrations, lack reliable knowledge basis and thus struggle for trustworthy responses in an automated pattern. While recent works endeavor to improve upon one certain aspect, they ignore the importance and necessity of establishing an integrated and interpretable reasoning system. To address these drawbacks and provide a universal solution, we propose AuRoRA: a one-for-all platform for augmented reasoning and refining based on CoT prompting that excels in adaptability, reliability, integrity, and interpretability. The system exhibits superior performances across six reasoning tasks and offers real-time visual analysis, which has pivotal academic and application value in the era of LLMs. The AuRoRA platform is available at https://huggingface.co/spaces/Anni123/AuRoRA.

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Automated Extraction of Prosodic Structure from Unannotated Sign Language Video
Antonio F. G. Sevilla | José María Lahoz-Bengoechea | Alberto Diaz

As in oral phonology, prosody is an important carrier of linguistic information in sign languages. One of the most prominent ways this reveals itself is in the time structure of signs: their rhythm and intensity of articulation. To be able to empirically see these effects, the velocity of the hands can be computed throughout the execution of a sign. In this article, we propose a method for extracting this information from unlabeled videos of sign language, exploiting CoTracker, a recent advancement in computer vision which can track every point in a video without the need of any calibration or fine-tuning. The dominant hand is identified via clustering of the computed point velocities, and its dynamic profile plotted to make apparent the prosodic structure of signing. We apply our method to different datasets and sign languages, and perform a preliminary visual exploration of results. This exploration supports the usefulness of our methodology for linguistic analysis, though issues to be tackled remain, such as bi-manual signs and a formal and numerical evaluation of accuracy. Nonetheless, the absence of any preprocessing requirements may make it useful for other researchers and datasets.

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Automatically Estimating Textual and Phonemic Complexity for Cued Speech: How to See the Sounds from French Texts
Núria Gala | Brigitte Bigi | Marie Bauer

In this position paper we present a methodology to automatically annotate French text for Cued Speech (CS), a communication system developed for people with hearing loss to complement speech reading at the phonetic level. This visual communication mode uses handshapes in different placements near the face in combination with the mouth movements (called ‘cues’ or ‘keys’) to make the phonemes of spoken language look different from each other. CS is used to acquire skills in lip reading, in oral communication and for reading. Despite many studies demonstrating its benefits, there are few resources available for learning and practicing it, especially in French. We thus propose a methodology to phonemize written corpora so that each word is aligned with the corresponding CS key(s). This methodology is proposed as part of a wider project aimed at creating an augmented reality system displaying a virtual coding hand where the user will be able to choose a text upon its complexity for cueing.

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Automatic Animacy Classification for Romanian Nouns
Maria Tepei | Jelke Bloem

We introduce the first Romanian animacy classifier, specifically a type-based binary classifier of Romanian nouns into the classes human/non-human, using pre-trained word embeddings and animacy information derived from Romanian WordNet. By obtaining a seed set of labeled nouns and their embeddings, we are able to train classifiers that generalize to unseen nouns. We compare three different architectures and observe good performance on classifying word types. In addition, we manually annotate a small corpus for animacy to perform a token-based evaluation of Romanian animacy classification in a naturalistic setting, which reveals limitations of the type-based classification approach.

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Automatic Annotation of Grammaticality in Child-Caregiver Conversations
Mitja Nikolaus | Abhishek Agrawal | Petros Kaklamanis | Alex Warstadt | Abdellah Fourtassi

The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools for automatic annotation can offer an effective alternative to tedious manual annotation. We propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate more than 4,000 utterances from a large corpus of transcribed conversations. Based on these annotations, we train and evaluate a range of NLP models. Our results show that fine-tuned Transformer-based models perform best, achieving human inter-annotation agreement levels. As a first application and sanity check of this tool, we use the trained models to annotate a corpus almost two orders of magnitude larger than the manually annotated data and verify that children’s grammaticality shows a steady increase with age. This work contributes to the growing literature on applying state-of-the-art NLP methods to help study child language acquisition at scale.

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Automatic Authorship Analysis in Human-AI Collaborative Writing
Aquia Richburg | Calvin Bao | Marine Carpuat

As the quality of AI-generated text increases with the development of new Large Language Models, people use them to write in a variety of contexts. Human-AI collaborative writing poses a potential challenge for existing AI analysis techniques, which have been primarily tested either on human-written text only, or on samples independently generated by humans and AI. In this work, we investigate the extent to which existing AI detection and authorship analysis models can perform classification on data generated in human-AI collaborative writing sessions. Results show that, for AI text detection in the cowriting setting, classifiers based on authorship embeddings (Rivera-Soto et al., 2021) outperform classifiers used in prior work distinguishing AI vs. human text generated independently. However, these embeddings are not optimal for finer-grained authorship identification tasks: for authorship verification, n-gram based models are more robust to human-AI co-written text, and authorship attribution performance degrades compared to baselines that use human-written text only. Taken together, this suggests that the rise of human-AI co-written text will require adapting AI detection tools and authorship analysis techniques in the near future. We release our code at https://github.com/AARichburg/Human-AI_Authorship_Analysis.

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Automatic Coding of Contingency in Child-Caregiver Conversations
Abhishek Agrawal | Mitja Nikolaus | Benoit Favre | Abdellah Fourtassi

One of the most important communicative skills children have to learn is to engage in meaningful conversations with people around them. At the heart of this learning lies the mastery of contingency, i.e., the ability to contribute to an ongoing exchange in a relevant fashion (e.g., by staying on topic). Current research on this question relies on the manual annotation of a small sample of children, which limits our ability to draw general conclusions about development. Here, we propose to mitigate the limitations of manual labor by relying on automatic tools for contingency judgment in children’s early natural interactions with caregivers. Drawing inspiration from the field of dialogue systems evaluation, we built and compared several automatic classifiers. We found that a Transformer-based pre-trained language model – when fine-tuned on a relatively small set of data we annotated manually (around 3,500 turns) – provided the best predictions. We used this model to automatically annotate, new and large-scale data, almost two orders of magnitude larger than our fine-tuning set. It was able to replicate existing results and generate new data-driven hypotheses. The broad impact of the work is to provide resources that can help the language development community study communicative development at scale, leading to more robust theories.

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Automatic Construction of a Chinese Review Dataset for Aspect Sentiment Triplet Extraction via Iterative Weak Supervision
Chia-Wen Lu | Ching-Wen Yang | Wei-Yun Ma

Aspect Sentiment Triplet Extraction (ASTE), introduced in 2020, is a task that involves the extraction of three key elements: target aspects, descriptive opinion spans, and their corresponding sentiment polarity. This process, however, faces a significant hurdle, particularly when applied to Chinese languages, due to the lack of sufficient datasets for model training, largely attributable to the arduous manual labeling process. To address this issue, we present an innovative framework that facilitates the automatic construction of ASTE via Iterative Weak Supervision, negating the need for manual labeling, aided by a discriminator to weed out subpar samples. The objective is to successively improve the quality of this raw data and generate supplementary data. The effectiveness of our approach is underscored by our results, which include the creation of a substantial Chinese review dataset. This dataset encompasses over 60,000 Google restaurant reviews in Chinese and features more than 200,000 extracted triplets. Moreover, we have also established a robust baseline model by leveraging a novel method of weak supervision. Both our dataset and model are openly accessible to the public.

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Automatic Construction of a Large-Scale Corpus for Geoparsing Using Wikipedia Hyperlinks
Keyaki Ohno | Hirotaka Kameko | Keisuke Shirai | Taichi Nishimura | Shinsuke Mori

Geoparsing is the task of estimating the latitude and longitude (coordinates) of location expressions in texts. Geoparsing must deal with the ambiguity of the expressions that indicate multiple locations with the same notation. For evaluating geoparsing systems, several corpora have been proposed in previous work. However, these corpora are small-scale and suffer from the coverage of location expressions on general domains. In this paper, we propose Wikipedia Hyperlink-based Location Linking (WHLL), a novel method to construct a large-scale corpus for geoparsing from Wikipedia articles. WHLL leverages hyperlinks in Wikipedia to annotate multiple location expressions with coordinates. With this method, we constructed the WHLL corpus, a new large-scale corpus for geoparsing. The WHLL corpus consists of 1.3M articles, each containing about 7.8 unique location expressions. 45.6% of location expressions are ambiguous and refer to more than one location with the same notation. In each article, location expressions of the article title and those hyperlinks to other articles are assigned with coordinates. By utilizing hyperlinks, we can accurately assign location expressions with coordinates even with ambiguous location expressions in the texts. Experimental results show that there remains room for improvement by disambiguating location expressions.

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Automatic Data Visualization Generation from Chinese Natural Language Questions
Yan Ge | Victor Junqiu Wei | Yuanfeng Song | Jason Chen Zhang | Raymond Chi-Wing Wong

Data visualization has emerged as an effective tool for getting insights from massive datasets. Due to the hardness of manipulating the programming languages of data visualization, automatic data visualization generation from natural languages (Text-to-Vis) is becoming increasingly popular. Despite the plethora of research effort on the English Text-to-Vis, studies have yet to be conducted on data visualization generation from questions in Chinese. Motivated by this, we propose a Chinese Text-to-Vis dataset in the paper and demonstrate our first attempt to tackle this problem. Our model integrates multilingual BERT as the encoder, boosts the cross-lingual ability, and infuses the n-gram information into our word representation learning. Our experimental results show that our dataset is challenging and deserves further research.

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Automatic Decomposition of Text Editing Examples into Primitive Edit Operations: Toward Analytic Evaluation of Editing Systems
Daichi Yamaguchi | Rei Miyata | Atsushi Fujita | Tomoyuki Kajiwara | Satoshi Sato

This paper presents our work on a task of automatic decomposition of text editing examples into primitive edit operations. Toward a detailed analysis of the behavior of text editing systems, identification of fine-grained edit operations performed by the systems is essential. Given a pair of source and edited sentences, the goal of our task is to generate a non-redundant sequence of primitive edit operations, i.e., the semantically minimal edit operations preserving grammaticality, that iteratively converts the source sentence to the edited sentence. First, we formalize this task, explaining its significant features and specifying the constraints that primitive edit operations should satisfy. Then, we propose a method to automate this task, which consists of two steps: generation of an edit operation lattice and selection of an optimal path. To obtain a wide range of edit operation candidates in the first step, we combine a phrase aligner and a large language model. Experimental results show that our method perfectly decomposes 44% and 64% of editing examples in the text simplification and machine translation post-editing datasets, respectively. Detailed analyses also provide insights into the difficulties of this task, suggesting directions for improvement.

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Automatic Extraction of Language-Specific Biomarkers of Healthy Aging in Icelandic
Elena Callegari | Iris Edda Nowenstein | Ingunn Jóhanna Kristjánsdóttir | Anton Karl Ingason

This study examines the influence of task type and healthy aging on various automatically extracted part-of-speech features in Icelandic. We administered three language tasks to participants aged 60–80: picture description, trip planning, and description of one’s childhood home. Our findings reveal significant task effects on 11 out of 14 linguistic variables studied, highlighting the substantial influence of sampling methods on language production. Among the variables showing statistically significant task effects, we find the rate of the genitive and subjunctive, variables which can only be studied in morphologically richer languages like Icelandic. On the other hand, rates of pronouns, adverbs, and prepositions remained stable across task types. Aging effects were more subtle, being evident in 3 of the 14 variables, including an interaction with task type for dative case marking. These findings underscore the significance of task selection in studies targeting linguistic features but also emphasize the need to examine languages other than English to fully understand the effects of aging on language production. Additionally, the results have clinical implications: understanding healthy aging’s impact on language can help us better identify and study changes caused by Alzheimer’s Disease in older adults’ speech.

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Automatic Extraction of Nominal Phrases from German Learner Texts of Different Proficiency Levels
Ronja Laarmann-Quante | Marco Müller | Eva Belke

Correctly inflecting determiners and adjectives so that they agree with the noun in nominal phrases (NPs) is a big challenge for learners of German. Given the increasing number of available learner corpora, a large-scale corpus-based study on the acquisition of this aspect of German morphosyntax would be desirable. In this paper, we present a pilot study in which we investigate how well nouns, their grammatical heads and the dependents that have to agree with the noun can be extracted automatically via dependency parsing. For six samples of the German learner corpus MERLIN (one per proficiency level), we found that in spite of many ungrammatical sentences in texts of low proficiency levels, human annotators find only few true ambiguities that would make the extraction of NPs and their heads infeasible. The automatic parsers, however, perform rather poorly on extracting the relevant elements for texts on CEFR levels A1-B1 (< 70%) but quite well from level B2 onwards ( 90%). We discuss the sources of errors and how performance could potentially be increased in the future.

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Automatic Identification of COVID-19-Related Conspiracy Narratives in German Telegram Channels and Chats
Philipp Heinrich | Andreas Blombach | Bao Minh Doan Dang | Leonardo Zilio | Linda Havenstein | Nathan Dykes | Stephanie Evert | Fabian Schäfer

We are concerned with mapping the discursive landscape of conspiracy narratives surrounding the COVID-19 pandemic. In the present study, we analyse a corpus of more than 1,000 German Telegram posts tagged with 14 fine-grained conspiracy narrative labels by three independent annotators. Since emerging narratives on social media are short-lived and notoriously hard to track, we experiment with different state-of-the-art approaches to few-shot and zero-shot text classification. We report performance in terms of ROC-AUC and in terms of optimal F1, and compare fine-tuned methods with off-the-shelf approaches and human performance.

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Automatic Partitioning of a Code-Switched Speech Corpus Using Mixed-Integer Programming
Joshua Miles Jansen van Vüren | Febe de Wet | Thomas Niesler

Defining training, development and test set partitions for speech corpora is usually accomplished by hand. However, for the dataset under investigation, which contains a large number of speakers, eight different languages and code-switching between all the languages, this style of partitioning is not feasible. Therefore, we view the partitioning task as a resource allocation problem and propose to solve it automatically and optimally by the application of mixed-integer linear programming. Using this approach, we are able to partition a new 41.6-hour multilingual corpus of code-switched speech into training, development and testing partitions while maintaining a fixed number of speakers and a specific amount of code-switched speech in the development and test partitions. For this newly partitioned corpus, we present baseline speech recognition results using a state-of-the-art multilingual transformer model (Wav2Vec2-XLS-R) and show that the exclusion of very short utterances (<1s) results in substantially improved speech recognition performance.

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Automatic Punctuation Model for Spanish Live Transcriptions
Mario Perez-Enriquez | Jose Manuel Masiello-Ruiz | Jose Luis Lopez-Cuadrado | Israel Gonzalez-Carrasco | Paloma Martinez-Fernandez | Belen Ruiz-Mezcua

With the widespread adoption of automatic transcription tools, acquiring speech transcriptions within seconds has become a reality. Nonetheless, many of these tools yield unpunctuated outputs, potentially incurring additional costs. This paper presents a novel approach to integrating punctuation into the transcriptions generated by such automatic tools, specifically focusing on Spanish-speaking contexts. Leveraging the RoBERTa-bne model pre-trained with data from the Spanish National Library, our training proposal is augmented with additional corpora to enhance performance on less common punctuation marks, such as question marks. Also, the proposed model has been trained through fine-tuning pre-trained models, involving adjustments for token classification and using SoftMax to identify the highest probability token. The proposed model obtains promising results when compared with other Spanish reference paper models. Ultimately, this model aims to facilitate punctuation on live transcriptions seamlessly and accurately. The proposed model will be applied to a real-case education project to improve the readability of the transcriptions.

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Automatic Speech Interruption Detection: Analysis, Corpus, and System
Martin Lebourdais | Marie Tahon | Antoine Laurent | Sylvain Meignier

Interruption detection is a new yet challenging task in the field of speech processing. This article presents a comprehensive study on automatic speech interruption detection, from the definition of this task, the assembly of a specialized corpus, and the development of an initial baseline system. We provide three main contributions: Firstly, we define the task, taking into account the nuanced nature of interruptions within spontaneous conversations. Secondly, we introduce a new corpus of conversational data, annotated for interruptions, to facilitate research in this domain. This corpus serves as a valuable resource for evaluating and advancing interruption detection techniques. Lastly, we present a first baseline system, which use speech processing methods to automatically identify interruptions in speech with promising results. In this article, we derivate from theoretical notions of interruption to build a simplification of this notion based on overlapped speech detection. Our findings can not only serve as a foundation for further research in the field but also provide a benchmark for assessing future advancements in automatic speech interruption detection.

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Automatic Speech Recognition for Gascon and Languedocian Variants of Occitan
Iñigo Morcillo | Igor Leturia | Ander Corral | Xabier Sarasola | Michaël Barret | Aure Séguier | Benaset Dazéas

This paper describes different approaches for developing, for the first time, an automatic speech recognition system for two of the main dialects of Occitan, namely Gascon and Languedocian, and the results obtained in them. The difficulty of the task lies in the fact that Occitan is a less-resourced language. Although a great effort has been made to collect or create corpora of each variant (transcribed speech recordings for the acoustic models and two text corpora for the language models), the sizes of the corpora obtained are far from those of successful systems reported in the literature, and thus we have tested different techniques to compensate for the lack of resources. We have developed classical systems using Kaldi, creating an acoustic model for each variant and also creating language models from the collected corpora and from machine translated texts. We have also tried fine-tuning a Whisper model with our speech corpora. We report word error rates of 20.86 for Gascon and 13.52 for Languedocian with the Kaldi systems and 16.37 for Gascon and 11.74 for Languedocian with Whisper.

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Automatic Speech Recognition System-Independent Word Error Rate Estimation
Chanho Park | Mingjie Chen | Thomas Hain

Word error rate (WER) is a metric used to evaluate the quality of transcriptions produced by Automatic Speech Recognition (ASR) systems. In many applications, it is of interest to estimate WER given a pair of a speech utterance and a transcript. Previous work on WER estimation focused on building models that are trained with a specific ASR system in mind (referred to as ASR system-dependent). These are also domain-dependent and inflexible in real-world applications. In this paper, a hypothesis generation method for ASR System-Independent WER estimation (SIWE) is proposed. In contrast to prior work, the WER estimators are trained using data that simulates ASR system output. Hypotheses are generated using phonetically similar or linguistically more likely alternative words. In WER estimation experiments, the proposed method reaches a similar performance to ASR system-dependent WER estimators on in-domain data and achieves state-of-the-art performance on out-of-domain data. On the out-of-domain data, the SIWE model outperformed the baseline estimators in root mean square error and Pearson correlation coefficient by relative 17.58% and 18.21%, respectively, on Switchboard and CALLHOME. The performance was further improved when the WER of the training set was close to the WER of the evaluation dataset.

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Automating Dataset Production Using Generative Text and Image Models
Christopher Thierauf | Mitchell Abrams | Matthias Scheutz

Practical and ethical dataset collection remains a challenge blocking many empirical methods in natural language processing, resulting in a lack of benchmarks or data on which to test hypotheses. We propose a solution to some of these areas by presenting a pipeline to reduce the research burden of producing image and text datasets when datasets may not exist. Our approach, with accompanying software tools, involves (1) generating text with LLMs; (2) creating accompanying image vignettes with text–to–image transformers; and (3) low-cost human validation. Based on existing literature that has struggled with quantitative evaluation (due to difficulty of data collection), we present the creation of 3 relevant datasets, and conduct a user study that demonstrates this approach is able to aid researchers in obtaining previously-challenging datasets. We provide sample data generated with this technique, the source code used to produce it, and discuss applicability and limitations.

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Autonomous Aspect-Image Instruction a2II: Q-Former Guided Multimodal Sentiment Classification
Junjia Feng | Mingqian Lin | Lin Shang | Xiaoying Gao

Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings.

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Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification
Xindi Wang | Robert E. Mercer | Frank Rudzicz

The International Classification of Diseases (ICD) is an authoritative medical classification system of different diseases and conditions for clinical and management purposes. ICD indexing aims to assign a subset of ICD codes to a medical record. Since human coding is labour-intensive and error-prone, many studies employ machine learning techniques to automate the coding process. ICD coding is a challenging task, as it needs to assign multiple codes to each medical document from an extremely large hierarchically organized collection. In this paper, we propose a novel approach for ICD indexing that adopts three ideas: (1) we use a multi-level deep dilated residual convolution encoder to aggregate the information from the clinical notes and learn document representations across different lengths of the texts; (2) we formalize the task of ICD classification with auxiliary knowledge of the medical records, which incorporates not only the clinical texts but also different clinical code terminologies and drug prescriptions for better inferring the ICD codes; and (3) we introduce a graph convolutional network to leverage the co-occurrence patterns among ICD codes, aiming to enhance the quality of label representations. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures.

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A Virtual Patient Dialogue System Based on Question-Answering on Clinical Records
Janire Arana | Mikel Idoyaga | Maitane Urruela | Elisa Espina | Aitziber Atutxa Salazar | Koldo Gojenola

In this work we present two datasets for the development of virtual patients and the first evaluation results. We firstly introduce a Spanish corpus of medical dialogue questions annotated with intents, built upon prior research in French. We also propose a second dataset of dialogues using a novel annotation approach that involves doctor questions, patient answers, and corresponding clinical records, organized as triples of the form (clinical report, question, patient answer). This way, the doctor-patient conversation is modeled as a question-answering system that tries to find responses to questions taking a clinical record as input. This approach can help to eliminate the need for manually structured patient records, as commonly used in previous studies, thereby expanding the pool of diverse virtual patients available. Leveraging these annotated corpora, we develop and assess an automatic system designed to answer medical dialogue questions posed by medical students to simulated patients in medical exams. Our approach demonstrates robust generalization, relying solely on medical records to generate new patient cases. The two datasets and the code will be freely available for the research community.

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A Web Portal about the State of the Art of NLP Tasks in Spanish
Enrique Amigó | Jorge Carrillo-de-Albornoz | Andrés Fernández | Julio Gonzalo | Guillermo Marco | Roser Morante | Laura Plaza | Jacobo Pedrosa

This paper presents a new web portal with information about the state of the art of natural language processing tasks in Spanish. It provides information about forums, competitions, tasks and datasets in Spanish, that would otherwise be spread in multiple articles and web sites. The portal consists of overview pages where information can be searched for and filtered by several criteria and individual pages with detailed information and hyperlinks to facilitate navigation. Information has been manually curated from publications that describe competitions and NLP tasks from 2013 until 2023 and will be updated as new tasks appear. A total of 185 tasks and 128 datasets from 94 competitions have been introduced.

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A Workflow for HTR-Postprocessing, Labeling and Classifying Diachronic and Regional Variation in Pre-Modern Slavic Texts
Piroska Lendvai | Maarten van Gompel | Anna Jouravel | Elena Renje | Uwe Reichel | Achim Rabus | Eckhart Arnold

We describe ongoing work for developing a workflow for the applied use case of classifying diachronic and regional language variation in Pre-Modern Slavic texts. The data were obtained via handwritten text recognition (HTR) on medieval manuscripts and printings and partly by manual transcription. Our goal is to develop a workflow for such historical language data, covering HTR-postprocessing, annotating and classifying the digitized texts. We test and adapt existing language resources to fit the pipeline with low-barrier tooling, accessible for Humanists with limited experience in research data infrastructures, computational analysis or advanced methods of natural language processing (NLP). The workflow starts by addressing ground truth (GT) data creation for diagnosing and correcting HTR errors via string metrics and data-driven methods. On GT and on HTR data, we subsequently show classification results using transfer learning on sentence-level text snippets. Next, we report on our token-level data labeling efforts. Each step of the workflow is complemented with describing current limitations and our corresponding work in progress.

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A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks
Yanis Labrak | Mickael Rouvier | Richard Dufour

The recent emergence of Large Language Models (LLMs) has enabled significant advances in the field of Natural Language Processing (NLP). While these new models have demonstrated superior performance on various tasks, their application and potential are still underexplored, both in terms of the diversity of tasks they can handle and their domain of application. In this context, we evaluate four state-of-the-art instruction-tuned LLMs (ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca) on a set of 13 real-world clinical and biomedical NLP tasks in English, including named-entity recognition (NER), question-answering (QA), relation extraction (RE), and more. Our overall results show that these evaluated LLMs approach the performance of state-of-the-art models in zero- and few-shot scenarios for most tasks, particularly excelling in the QA task, even though they have never encountered examples from these tasks before. However, we also observe that the classification and RE tasks fall short of the performance achievable with specifically trained models designed for the medical field, such as PubMedBERT. Finally, we note that no single LLM outperforms all others across all studied tasks, with some models proving more suitable for certain tasks than others.

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Backdoor NLP Models via AI-Generated Text
Wei Du | Tianjie Ju | Ge Ren | GaoLei Li | Gongshen Liu

Backdoor attacks pose a critical security threat to natural language processing (NLP) models by establishing covert associations between trigger patterns and target labels without affecting normal accuracy. Existing attacks usually disregard fluency and semantic fidelity of poisoned text, rendering the malicious data easily detectable. However, text generation models can produce coherent and content-relevant text given prompts. Moreover, potential differences between human-written and AI-generated text may be captured by NLP models while being imperceptible to humans. More insidious threats could arise if attackers leverage latent features of AI-generated text as trigger patterns. We comprehensively investigate backdoor attacks on NLP models using AI-generated poisoned text obtained via continued writing or paraphrasing, exploring three attack scenarios: data, model and pre-training. For data poisoning, we fine-tune generators with attribute control to enhance the attack performance. For model poisoning, we leverage downstream tasks to derive specialized generators. For pre-training poisoning, we train multiple attribute-based generators and align their generated text with pre-defined vectors, enabling task-agnostic migration attacks. Experiments demonstrate that our method achieves effective attacks while maintaining fluency and semantic similarity across all scenarios. We hope this work can raise awareness of the security risks hidden in AI-generated text.

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BalsuTalka.lv - Boosting the Common Voice Corpus for Low-Resource Languages
Roberts Dargis | Arturs Znotins | Ilze Auzina | Baiba Saulite | Sanita Reinsone | Raivis Dejus | Antra Klavinska | Normunds Gruzitis

Open speech corpora of substantial size are seldom available for less-spoken languages, and this was recently the case also for Latvian with its 1.5M native speakers. While there exist several closed Latvian speech corpora of 100+ hours, used to train competitive models for automatic speech recognition (ASR), there were only a few tiny open datasets available at the beginning of 2023, the 18-hour Latvian Common Voice 13.0 dataset being the largest one. In the result of a successful national crowdsourcing initiative, organised jointly by several institutions, the size and speaker diversity of the Latvian Common Voice 17.0 release have increased more than tenfold in less than a year. A successful follow-up initiative was also launched for Latgalian, which has been recognized as an endangered historic variant of Latvian with 150k speakers. The goal of these initiatives is not only to enlarge the datasets but also to make them more diverse in terms of speakers and accents, text genres and styles, intonations, grammar and lexicon. They have already become considerable language resources for both improving ASR and conducting linguistic research. Since we use the Mozilla Common Voice platform to record and validate speech samples, this paper focuses on (i) the selection of text snippets to enrich the language data and to stimulate various intonations, (ii) an indicative evaluation of the acquired corpus and the first ASR models fine-tuned on this data, (iii) our social campaigns to boost and maintain this initiative.

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BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models
Zican Dong | Tianyi Tang | Junyi Li | Wayne Xin Zhao | Ji-Rong Wen

Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with normal length. Recently, multiple studies have committed to extending the context length and enhancing the long text modeling capabilities of LLMs. To comprehensively evaluate the long context ability of LLMs, we propose BAMBOO, a multi-task long context benchmark. BAMBOO has been designed with four principles: comprehensive capacity evaluation, avoidance of data contamination, accurate automatic evaluation, and different length levels. It consists of 10 datasets from 5 different long text understanding tasks, i.e., question answering, hallucination detection, text sorting, language modeling, and code completion, to cover various domains and core capacities of LLMs. We conduct experiments with five widely-used long-context models and further discuss five key questions for long text research. In the end, we discuss problems of current long-context models and point out future directions for enhancing long text modeling capacities. We release our data, prompts, and code at https://anonymous.4open.science/r/BAMBOO/.

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BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering
Azmine Toushik Wasi | Taki Hasan Rafi | Raima Islam | Dong-Kyu Chae

Knowledge Graphs (KGs) have proven essential in information processing and reasoning applications because they link related entities and give context-rich information, supporting efficient information retrieval and knowledge discovery; presenting information flow in a very effective manner. Despite being widely used globally, Bangla is relatively underrepresented in KGs due to a lack of comprehensive datasets, encoders, NER (named entity recognition) models, POS (part-of-speech) taggers, and lemmatizers, hindering efficient information processing and reasoning applications in the language. Addressing the KG scarcity in Bengali, we propose BanglaAutoKG, a pioneering framework that is able to automatically construct Bengali KGs from any Bangla text. We utilize multilingual LLMs to understand various languages and correlate entities and relations universally. By employing a translation dictionary to identify English equivalents and extracting word features from pre-trained BERT models, we construct the foundational KG. To reduce noise and align word embeddings with our goal, we employ graph-based polynomial filters. Lastly, we implement a GNN-based semantic filter, which elevates contextual understanding and trims unnecessary edges, culminating in the formation of the definitive KG. Empirical findings and case studies demonstrate the universal effectiveness of our model, capable of autonomously constructing semantically enriched KGs from any text. Data and code are available here: https://github.com/azminewasi/BanglaAutoKG

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BAN-PL: A Polish Dataset of Banned Harmful and Offensive Content from Wykop.pl Web Service
Anna Kolos | Inez Okulska | Kinga Głąbińska | Agnieszka Karlinska | Emilia Wisnios | Paweł Ellerik | Andrzej Prałat

Since the Internet is flooded with hate, it is one of the main tasks for NLP experts to master automated online content moderation. However, advancements in this field require improved access to publicly available accurate and non-synthetic datasets of social media content. For the Polish language, such resources are very limited. In this paper, we address this gap by presenting a new open dataset of offensive social media content for the Polish language. The dataset comprises content from Wykop.pl, a popular online service often referred to as the Polish Reddit, reported by users and banned in the internal moderation process. It contains a total of 691,662 posts and comments, evenly divided into two categories: harmful and neutral (non-harmful). The anonymized subset of the BAN-PL dataset consisting on 24,000 pieces (12,000 for each class), along with preprocessing scripts have been made publicly available. Furthermore the paper offers valuable insights into real-life content moderation processes and delves into an analysis of linguistic features and content characteristics of the dataset. Moreover, a comprehensive anonymization procedure has been meticulously described and applied. The prevalent biases encountered in similar datasets, including post-moderation and pre-selection biases, are also discussed.

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“Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning
JingJie Zeng | Liang Yang | Jiahao Kang | Yufeng Diao | Zhihao Yang | Hongfei Lin

In the realm of artificial intelligence and linguistics, the automatic generation of humor, particularly puns, remains a complex task. This paper introduces an innovative approach that employs a Generative Adversarial Network (GAN) and semantic pruning techniques to generate humorous puns. We initiate our process by identifying potential pun candidates via semantic pruning. This is followed by the use of contrastive learning to decode the unique characteristics of puns, emphasizing both correct and incorrect interpretations. The learned features from contrastive learning are utilized within our GAN model to better capture the semantic nuances of puns. Specifically, the generator exploits the pruned semantic tree to generate pun texts, while the discriminator evaluates the generated puns, ensuring both linguistic correctness and humor. Evaluation results highlight our model’s capacity to produce semantically coherent and humorous puns, demonstrating an enhancement over prior methods and approach human-level performance. This work contributes significantly to the field of computational humor, advancing the capabilities of automatic pun generation.

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Basque and Spanish Counter Narrative Generation: Data Creation and Evaluation
Jaione Bengoetxea | Yi-Ling Chung | Marco Guerini | Rodrigo Agerri

Counter Narratives (CNs) are non-negative textual responses to Hate Speech (HS) aiming at defusing online hatred and mitigating its spreading across media. Despite the recent increase in HS content posted online, research on automatic CN generation has been relatively scarce and predominantly focused on English. In this paper, we present CONAN-EUS, a new Basque and Spanish dataset for CN generation developed by means of Machine Translation (MT) and professional post-edition. Being a parallel corpus, also with respect to the original English CONAN, it allows to perform novel research on multilingual and crosslingual automatic generation of CNs. Our experiments on CN generation with mT5, a multilingual encoder-decoder model, shows that generation greatly benefits from training on post-edited data, as opposed to relying on silver MT data only. These results are confirmed by their correlation with a qualitative manual evaluation, demonstrating that manually revised training data remains crucial for the quality of the generated CNs. Furthermore, multilingual data augmentation improves results over monolingual settings for structurally similar languages such as English and Spanish, while being detrimental for Basque, a language isolate. Similar findings occur in zero-shot crosslingual evaluations, where model transfer (fine-tuning in English and generating in a different target language) outperforms fine-tuning mT5 on machine translated data for Spanish but not for Basque. This provides an interesting insight into the asymmetry in the multilinguality of generative models, a challenging topic which is still open to research. Data and code will be made publicly available upon publication.

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Becoming a High-Resource Language in Speech: The Catalan Case in the Common Voice Corpus
Carme Armentano-Oller | Montserrat Marimon | Marta Villegas

Collecting voice resources for speech recognition systems is a multifaceted challenge, involving legal, technical, and diversity considerations. However, it is crucial to ensure fair access to voice-driven technology across diverse linguistic backgrounds. We describe an ongoing effort to create an extensive, high-quality, publicly available voice dataset for future development of speech technologies in Catalan through the Mozilla Common Voice crowd-sourcing platform. We detail the specific approaches used to address the challenges faced in recruiting contributors and managing the collection, validation, and recording of sentences. This detailed overview can serve as a source of guidance for similar initiatives across other projects and linguistic contexts. The success of this project is evident in the latest corpus release, version 16.1, where Catalan ranks as the most prominent language in the corpus, both in terms of recorded hours and when considering validated hours. This establishes Catalan as a language with significant speech resources for language technology development and significantly raises its international visibility.

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BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language
Konrad Wojtasik | Kacper Wołowiec | Vadim Shishkin | Arkadiusz Janz | Maciej Piasecki

The BEIR dataset is a large, heterogeneous benchmark for Information Retrieval (IR), garnering considerable attention within the research community. However, BEIR and analogous datasets are predominantly restricted to English language. Our objective is to establish extensive large-scale resources for IR in the Polish language, thereby advancing the research in this NLP area. In this work, inspired by mMARCO and Mr. TyDi datasets, we translated all accessible open IR datasets into Polish, and we introduced the BEIR-PL benchmark – a new benchmark which comprises 13 datasets, facilitating further development, training and evaluation of modern Polish language models for IR tasks. We executed an evaluation and comparison of numerous IR models on the newly introduced BEIR-PL benchmark. Furthermore, we publish pre-trained open IR models for Polish language, marking a pioneering development in this field. The BEIR-PL is included in MTEB Benchmark and also available with trained models at URL https://huggingface.co/clarin-knext.

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Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies
Flavio Petruzzellis | Alberto Testolin | Alessandro Sperduti

Large Language Models (LLMs) have revolutionized the field of Natural Language Processing thanks to their ability to reuse knowledge acquired on massive text corpora on a wide variety of downstream tasks, with minimal (if any) tuning steps. At the same time, it has been repeatedly shown that LLMs lack systematic generalization, which allows to extrapolate the learned statistical regularities outside the training distribution. In this work, we offer a systematic benchmarking of GPT-4, one of the most advanced LLMs available, on three algorithmic tasks characterized by the possibility to control the problem difficulty with two parameters. We compare the performance of GPT-4 with that of its predecessor (GPT-3.5) and with a variant of the Transformer-Encoder architecture recently introduced to solve similar tasks, the Neural Data Router. We find that the deployment of advanced prompting techniques allows GPT-4 to reach superior accuracy on all tasks, demonstrating that state-of-the-art LLMs constitute a very strong baseline also in challenging tasks that require systematic generalization.

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Benchmarking Hallucination in Large Language Models Based on Unanswerable Math Word Problem
YuHong Sun | Zhangyue Yin | Qipeng Guo | Jiawen Wu | Xipeng Qiu | Hui Zhao

Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous contexts called hallucination. This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP). To support this approach, we innovatively develop a dataset called Unanswerable Math Word Problem (UMWP) which comprises 5200 questions across five categories. We developed an evaluation methodology combining text similarity and mathematical expression detection to determine whether LLM considers the question unanswerable. The results of extensive experiments conducted on 31 LLMs, including GPT-3, InstructGPT, LLaMA, and Claude, demonstrate that in-context learning and reinforcement learning with human feedback (RLHF) training significantly enhance the model’s ability to avoid hallucination. We show that utilizing MWP is a reliable and effective approach to assess hallucination. Our code and data are available at https://github.com/Yuki-Asuuna/UMWP.

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Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT
Amirhossein Abaskohi | Sara Baruni | Mostafa Masoudi | Nesa Abbasi | Mohammad Hadi Babalou | Ali Edalat | Sepehr Kamahi | Samin Mahdizadeh Sani | Nikoo Naghavian | Danial Namazifard | Pouya Sadeghi | Yadollah Yaghoobzadeh

This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for more low-resource languages remains an open question. We present the first comprehensive benchmarking study of LLMs across diverse Persian language tasks. Our primary focus is on GPT-3.5-turbo, but we also include GPT-4 and OpenChat-3.5 to provide a more holistic evaluation. Our assessment encompasses a diverse set of tasks categorized into classic, reasoning, and knowledge-based domains. To enable a thorough comparison, we evaluate LLMs against existing task-specific fine-tuned models. Given the limited availability of Persian datasets for reasoning tasks, we introduce two new benchmarks: one based on elementary school math questions and another derived from the entrance exams for 7th and 10th grades. Our findings reveal that while LLMs, especially GPT-4, excel in tasks requiring reasoning abilities and a broad understanding of general knowledge, they often lag behind smaller pretrained models fine-tuned specifically for particular tasks. Additionally, we observe improved performance when test sets are translated to English before inputting them into GPT-3.5. These results highlight the significant potential for enhancing LLM performance in the Persian language. This is particularly noteworthy due to the unique attributes of Persian, including its distinct alphabet and writing styles. We have made our codes, prompts, and data available here: https://github.com/Ipouyall/Benchmarking_ChatGPT_for_Persian.

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Benchmarking the Performance of Machine Translation Evaluation Metrics with Chinese Multiword Expressions
Huacheng Song | Hongzhi Xu

To investigate the impact of Multiword Expressions (MWEs) on the fine-grained performance of the state-of-the-art metrics for Machine Translation Evaluation (MTE), we conduct experiments on the WMT22 Metrics Shared Task dataset with a preliminary focus on the Chinese-to-English language pair. We further annotate 28 types of Chinese MWEs on the source texts and then examine the performance of 31 MTE metrics on groups of sentences containing different MWEs. We have 3 interesting findings: 1) Machine Translation (MT) systems tend to perform worse on most Chinese MWE categories, confirming the previous claim that MWEs are a bottleneck of MT; 2) automatic metrics tend to overrate the translation of sentences containing MWEs; 3) most neural-network-based metrics perform better than string-overlap-based metrics. It concludes that both MT systems and MTE metrics still suffer from MWEs, suggesting richer annotation of data to facilitate MWE-aware automatic MTE and MT.

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Benchmarking the Simplification of Dutch Municipal Text
Daniel Vlantis | Iva Gornishka | Shuai Wang

Text simplification (TS) makes written information more accessible to all people, especially those with cognitive or language impairments. Despite much progress in TS due to advances in NLP technology, the bottleneck issue of lack of data for low-resource languages persists. Dutch is one of these languages that lack a monolingual simplification corpus. In this paper, we use English as a pivot language for the simplification of Dutch medical and municipal text. We experiment with augmenting training data and corpus choice for this pivot-based approach. We compare the results to a baseline and an end-to-end LLM approach using the GPT 3.5 Turbo model. Our evaluation shows that, while we can substantially improve the results of the pivot pipeline, the zero-shot end-to-end GPT-based simplification performs better on all metrics. Our work shows how an existing pivot-based pipeline can be improved for simplifying Dutch medical text. Moreover, we provide baselines for the comparison in the domain of Dutch municipal text and make our corresponding evaluation dataset publicly available.

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BengaliLCP: A Dataset for Lexical Complexity Prediction in the Bengali Texts
Nabila Ayman | Md. Akram Hossain | Abdul Aziz | Rokan Uddin Faruqui | Abu Nowshed Chy

Encountering intricate or ambiguous terms within a sentence produces distress for the reader during comprehension. Lexical Complexity Prediction (LCP) deals with predicting the complexity score of a word or a phrase considering its context. This task poses several challenges including ambiguity, context sensitivity, and subjectivity in perceiving complexity. Despite having 300 million native speakers and ranking as the seventh most spoken language in the world, Bengali falls behind in the research on lexical complexity when compared to other languages. To bridge this gap, we introduce the first annotated Bengali dataset, that assists in performing the task of LCP in this language. Besides, we propose a transformer-based deep neural approach with a pairwise multi-head attention mechanism and LSTM model to predict the lexical complexity of Bengali tokens. The outcomes demonstrate that the proposed neural approach outperformed the existing state-of-the-art models for the Bengali language.

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BenLLM-Eval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP
Mohsinul Kabir | Mohammed Saidul Islam | Md Tahmid Rahman Laskar | Mir Tafseer Nayeem | M Saiful Bari | Enamul Hoque

Large Language Models (LLMs) have emerged as one of the most important breakthroughs in natural language processing (NLP) for their impressive skills in language generation and other language-specific tasks. Though LLMs have been evaluated in various tasks, mostly in English, they have not yet undergone thorough evaluation in under-resourced languages such as Bengali (Bangla). To this end, this paper introduces BenLLM-Eval, which consists of a comprehensive evaluation of LLMs to benchmark their performance in the low-resourced Bangla language. In this regard, we select various important and diverse Bangla NLP tasks, such as text summarization, question answering, paraphrasing, natural language inference, text classification, and sentiment analysis for zero-shot evaluation of popular LLMs, namely, ChatGPT, LLaMA-2, and Claude-2. Our experimental results demonstrate that while in some Bangla NLP tasks, zero-shot LLMs could achieve performance on par, or even better than current SOTA fine-tuned models; in most tasks, their performance is quite poor (with the performance of open-source LLMs like LLaMA-2 being significantly bad) in comparison to the current SOTA results. Therefore, it calls for further efforts to develop a better understanding of LLMs in low-resource languages like Bangla.

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BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection
Zhenfei Yang | Beiming Yu | Yuan Cui | Shi Feng | Daling Wang | Yifei Zhang

Recently, we have witnessed a significant performance boosting for dialogue response selection task achieved by Cross-Encoder based models. However, such models directly feed the concatenation of context and response into the pre-trained model for interactive inference, ignoring the comprehensively independent representation modeling of context and response. Moreover, randomly sampling negative responses from other dialogue contexts is simplistic, and the learned models have poor generalization capability in realistic scenarios. In this paper, we propose a response selection model called BERT-BC that combines the representation-based Bi-Encoder and interaction-based Cross-Encoder. Three contrastive learning methods are devised for the Bi-Encoder to align context and response to obtain the better semantic representation. Meanwhile, according to the alignment difficulty of context and response semantics, the harder samples are dynamically selected from the same batch with negligible cost and sent to Cross-Encoder to enhance the model’s interactive reasoning ability. Experimental results show that BERT-BC can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection.

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Beyond Binary: Towards Embracing Complexities in Cyberbullying Detection and Intervention - a Position Paper
Kanishk Verma | Kolawole John Adebayo | Joachim Wagner | Megan Reynolds | Rebecca Umbach | Tijana Milosevic | Brian Davis

In the digital age, cyberbullying (CB) poses a significant concern, impacting individuals as early as primary school and leading to severe or lasting consequences, including an increased risk of self-harm. CB incidents, are not limited to bullies and victims, but include bystanders with various roles, and usually have numerous sub-categories and variations of online harms. This position paper emphasises the complexity of CB incidents by drawing on insights from psychology, social sciences, and computational linguistics. While awareness of CB complexities is growing, existing computational techniques tend to oversimplify CB as a binary classification task, often relying on training datasets that capture peripheries of CB behaviours. Inconsistent definitions and categories of CB-related online harms across various platforms further complicates the issue. Ethical concerns arise when CB research involves children to role-play CB incidents to curate datasets. Through multi-disciplinary collaboration, we propose strategies for consideration when developing CB detection systems. We present our position on leveraging large language models (LLMs) such as Claude-2 and Llama2-Chat as an alternative approach to generate CB-related role-playing datasets. Our goal is to assist researchers, policymakers, and online platforms in making informed decisions regarding the automation of CB incident detection and intervention. By addressing these complexities, our research contributes to a more nuanced and effective approach to combating CB especially in young people.

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Beyond Canonical Fine-tuning: Leveraging Hybrid Multi-Layer Pooled Representations of BERT for Automated Essay Scoring
Eujene Nikka V. Boquio | Prospero C. Naval, Jr.

The challenging yet relevant task of automated essay scoring (AES) continuously gains attention from multiple disciplines over the years. With the advent of pre-trained large language models such as BERT, fine-tuning those models has become the dominant technique in various natural language processing (NLP) tasks. Several studies fine-tune BERT for the AES task but only utilize the final pooled output from its last layer. With BERT’s multi-layer architecture that encodes hierarchical linguistic information, we believe we can improve overall essay scoring performance by leveraging information from its intermediate layers. In this study, we diverge from the canonical fine-tuning paradigm by exploring different combinations of model outputs and single- and multi-layer pooling strategies, as well as architecture modifications to the task-specific component of the model. Using a hybrid pooling strategy, experimental results show that our best essay representa- tion combined with a simple architectural modification outperforms the average QWK score of the basic fine-tuned BERT with default output on the ASAP AES dataset, suggesting its effectiveness for the AES task and potentially other long-text tasks.

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Beyond Code: Evaluate Thought Steps for Complex Code Generation
Liuwen Cao | Yi Cai | Jiexin Wang | Hongkui He | Hailin Huang

Code generation aims to generate code in a general-purpose programming language, such as C++, based on natural language intents. Existing efforts primarily focus on relatively simple programming problems and fail to evaluate the thought process involved in complex programming scenarios. In this paper, we introduce “steps-guided code generation,” a task that assesses the quality of both thought steps and code implementation to evaluate the overall management of handling a complex programming problem. To support this task, we construct CodeStepsEval, a real-world scenario dataset of complex programming problems in the C++ programming language with varying levels of difficulty. Comprehensive experiments on this dataset demonstrate the importance of high-quality steps in enhancing code generation performance and the challenges faced by the code LLMs in this task.

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Beyond Full Fine-tuning: Harnessing the Power of LoRA for Multi-Task Instruction Tuning
Chunlei Xin | Yaojie Lu | Hongyu Lin | Shuheng Zhou | Huijia Zhu | Weiqiang Wang | Zhongyi Liu | Xianpei Han | Le Sun

Low-Rank Adaptation (LoRA) is a widespread parameter-efficient fine-tuning algorithm for large-scale language models. It has been commonly accepted that LoRA mostly achieves promising results in single-task, low-resource settings, and struggles to handle multi-task instruction tuning scenarios. In this paper, we conduct a systematic study of LoRA on diverse tasks and rich resources with different learning capacities, examining its performance on seen tasks during training and its cross-task generalization on unseen tasks. Our findings challenge the prevalent assumption that the limited learning capacity will inevitably result in performance decline. In fact, our study reveals that when configured with an appropriate rank, LoRA can achieve remarkable performance in high-resource and multi-task scenarios, even comparable to that achieved through full fine-tuning. It turns out that the constrained learning capacity encourages LoRA to prioritize conforming to instruction requirements rather than memorizing specialized features of particular tasks or instances. This study reveals the underlying connection between learning capacity and generalization capabilities for robust parameter-efficient fine-tuning, highlighting a promising direction for the broader application of LoRA across various tasks and settings.

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Beyond Linguistic Cues: Fine-grained Conversational Emotion Recognition via Belief-Desire Modelling
Bo Xu | Longjiao Li | Wei Luo | Mehdi Naseriparsa | Zhehuan Zhao | Hongfei Lin | Feng Xia

Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers. Although previous studies have made significant progress, accurate recognition and interpretation of similar fine-grained emotion properly accounting for individual variability remains a challenge. One particular under-explored area is the role of individual beliefs and desires in modelling emotion. Inspired by the Belief-Desire Theory of Emotion, we propose a novel method for conversational emotion recognition that incorporates both belief and desire to accurately identify emotions. We extract emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations. By applying message passing between nodes, our graph effectively models the utterance context, speaker’s global state, and the interaction between emotional beliefs, desires, and utterances. We evaluate our model’s performance by conducting extensive experiments on four popular ERC datasets and comparing it with multiple state-of-the-art models. The experimental results demonstrate the superiority of our proposed model and validate the effectiveness of each module in the model.

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Beyond Model Performance: Can Link Prediction Enrich French Lexical Graphs?
Hee-Soo Choi | Priyansh Trivedi | Mathieu Constant | Karen Fort | Bruno Guillaume

This paper presents a resource-centric study of link prediction approaches over French lexical-semantic graphs. Our study incorporates two graphs, RezoJDM16k and RL-fr, and we evaluated seven link prediction models, with CompGCN-ConvE emerging as the best performer. We also conducted a qualitative analysis of the predictions using manual annotations. Based on this, we found that predictions with higher confidence scores were more valid for inclusion. Our findings highlight different benefits for the dense graph compared to the sparser graph RL-fr. While the addition of new triples to RezoJDM16k offers limited advantages, RL-fr can benefit substantially from our approach.

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Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants’ API Invocation Capabilities
Honglin Mu | Yang Xu | Yunlong Feng | Xiaofeng Han | Yitong Li | Yutai Hou | Wanxiang Che

With the rise of Large Language Models (LLMs), AI assistants’ ability to utilize tools, especially through API calls, has advanced notably. This progress has necessitated more accurate evaluation methods. Many existing studies adopt static evaluation, where they assess AI assistants’ API call based on pre-defined dialogue histories. However, such evaluation method can be misleading, as an AI assistant might fail in generating API calls from preceding human interaction in real cases. Instead of the resource-intensive method of direct human-machine interactions, we propose Automated Dynamic Evaluation (AutoDE) to assess an assistant’s API call capability without human involvement. In our framework, we endeavor to closely mirror genuine human conversation patterns in human-machine interactions, using a LLM-based user agent, equipped with a user script to ensure human alignment. Experimental results highlight that AutoDE uncovers errors overlooked by static evaluations, aligning more closely with human assessment. Testing four AI assistants using our crafted benchmark, our method further mirrored human evaluation compared to conventional static evaluations.

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Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection
Pei Wang | Keqing He | Yejie Wang | Xiaoshuai Song | Yutao Mou | Jingang Wang | Yunsen Xian | Xunliang Cai | Weiran Xu

Out-of-domain (OOD) intent detection aims to examine whether the user’s query falls outside the predefined domain of the system, which is crucial for the proper functioning of task-oriented dialogue (TOD) systems. Previous methods address it by fine-tuning discriminative models. Recently, some studies have been exploring the application of large language models (LLMs) represented by ChatGPT to various downstream tasks, but it is still unclear for their ability on OOD detection task.This paper conducts a comprehensive evaluation of LLMs under various experimental settings, and then outline the strengths and weaknesses of LLMs. We find that LLMs exhibit strong zero-shot and few-shot capabilities, but is still at a disadvantage compared to models fine-tuned with full resource. More deeply, through a series of additional analysis experiments, we discuss and summarize the challenges faced by LLMs and provide guidance for future work including injecting domain knowledge, strengthening knowledge transfer from IND(In-domain) to OOD, and understanding long instructions.

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Beyond Words: Decoding Facial Expression Dynamics in Motivational Interviewing
Nezih Younsi | Catherine Pelachaud | Laurence Chaby

Authors : Nezih Younsi, Catherine Pelachaud, Laurence Chaby Title : Beyond Words: Decoding Facial Expression Dynamics in Motivational Interviewing Abstract : This paper focuses on studying the facial expressions of both client and therapist in the context of Motivational Interviewing (MI). The annotation system Motivational Interview Skill Code MISC defines three types of talk, namely sustain, change, and neutral for the client and information, question, or reflection for the therapist. Most studies on MI look at the verbal modality. Our research aims to understand the variation and dynamics of facial expressions of both interlocutors over a counseling session. We apply a sequence mining algorithm to identify categories of facial expressions for each type. Using co-occurrence analysis, we derive the correlation between the facial expressions and the different types of talk, as well as the interplay between interlocutors’ expressions.

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BigNLI: Native Language Identification with Big Bird Embeddings
Sergey Kramp | Giovanni Cassani | Chris Emmery

Native Language Identification (NLI) intends to classify an author’s native language based on their writing in another language. Historically, the task has heavily relied on time-consuming linguistic feature engineering, and NLI transformer models have thus far failed to offer effective, practical alternatives. The current work shows input size is a limiting factor, and that classifiers trained using Big Bird embeddings outperform linguistic feature engineering models (for which we reproduce previous work) by a large margin on the Reddit-L2 dataset. Additionally, we provide further insight into input length dependencies, show consistent out-of-sample (Europe subreddit) and out-of-domain (TOEFL-11) performance, and qualitatively analyze the embedding space. Given the effectiveness and computational efficiency of this method, we believe it offers a promising avenue for future NLI work.

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Biomedical Concept Normalization over Nested Entities with Partial UMLS Terminology in Russian
Natalia Loukachevitch | Andrey Sakhovskiy | Elena Tutubalina

We present a new manually annotated dataset of PubMed abstracts for concept normalization in Russian. It contains over 23,641 entity mentions in 756 documents linked to 4,544 unique concepts from the UMLS ontology. Compared to existing corpora, we explore two novel annotation characteristics: the nestedness of named entities and the incompleteness of the Russian medical terminology in UMLS. 4,424 entity mentions are linked to 1,535 unique English concepts absent in the Russian part of the UMLS ontology. We present several baselines for normalization over nested named entities obtained with state-of-the-art models such as SapBERT. Our experimental results show that models pre-trained on graph structural data from UMLS achieve superior performance in a zero-shot setting on bilingual terminology.

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Biomedical Entity Linking as Multiple Choice Question Answering
Zhenxi Lin | Ziheng Zhang | Xian Wu | Yefeng Zheng

Although biomedical entity linking (BioEL) has made significant progress with pre-trained language models, challenges still exist for fine-grained and long-tailed entities. To address these challenges, we present BioELQA, a novel model that treats Biomedical Entity Linking as Multiple Choice Question Answering. BioELQA first obtains candidate entities with a fast retriever, jointly presents the mention and candidate entities to a generator, and then outputs the predicted symbol associated with its chosen entity. This formulation enables explicit comparison of different candidate entities, thus capturing fine-grained interactions between mentions and entities, as well as among entities themselves. To improve generalization for long-tailed entities, we retrieve similar labeled training instances as clues and concatenate the input with retrieved instances for the generator. Extensive experimental results show that BioELQA outperforms state-of-the-art baselines on several datasets.

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Bits and Pieces: Investigating the Effects of Subwords in Multi-task Parsing across Languages and Domains
Daniel Dakota | Sandra Kübler

Neural parsing is very dependent on the underlying language model. However, very little is known about how choices in the language model affect parsing performance, especially in multi-task learning. We investigate questions on how the choice of subwords affects parsing, how subword sharing is responsible for gains or negative transfer in a multi-task setting where each task is parsing of a specific domain of the same language. More specifically, we investigate these issues across four languages: English, German, Italian, and Turkish. We find a general preference for averaged or last subwords across languages and domains. However, specific POS tags may require different subwords, and the distributional overlap between subwords across domains is perhaps a more influential factor in determining positive or negative transfer than discrepancies in the data sizes.

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BiVert: Bidirectional Vocabulary Evaluation Using Relations for Machine Translation
Carinne Cherf | Yuval Pinter

Neural machine translation (NMT) has progressed rapidly in the past few years, promising improvements and quality translations for different languages. Evaluation of this task is crucial to determine the quality of the translation. Overall, insufficient emphasis is placed on the actual sense of the translation in traditional methods. We propose a bidirectional semantic-based evaluation method designed to assess the sense distance of the translation from the source text. This approach employs the comprehensive multilingual encyclopedic dictionary BabelNet. Through the calculation of the semantic distance between the source and its back translation of the output, our method introduces a quantifiable approach that empowers sentence comparison on the same linguistic level. Factual analysis shows a strong correlation between the average evaluation scores generated by our method and the human assessments across various machine translation systems for English-German language pair. Finally, our method proposes a new multilingual approach to rank MT systems without the need for parallel corpora.

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BKEE: Pioneering Event Extraction in the Vietnamese Language
Thi-Nhung Nguyen | Bang Tien Tran | Trong-Nghia Luu | Thien Huu Nguyen | Kiem-Hieu Nguyen

Event Extraction (EE) is a fundamental task in information extraction, aimed at identifying events and their associated arguments within textual data. It holds significant importance in various applications and serves as a catalyst for the development of related tasks. Despite the availability of numerous datasets and methods for event extraction in various languages, there has been a notable absence of a dedicated dataset for the Vietnamese language. To address this limitation, we propose BKEE, a novel event extraction dataset for Vietnamese. BKEE encompasses over 33 distinct event types and 28 different event argument roles, providing a labeled dataset for entity mentions, event mentions, and event arguments on 1066 documents. Additionally, we establish robust baselines for potential downstream tasks on this dataset, facilitating the analysis of challenges and future development prospects in the field of Vietnamese event extraction.

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BlendX: Complex Multi-Intent Detection with Blended Patterns
Yejin Yoon | Jungyeon Lee | Kangsan Kim | Chanhee Park | Taeuk Kim

Task-oriented dialogue (TOD) systems are commonly designed with the presumption that each utterance represents a single intent. However, this assumption may not accurately reflect real-world situations, where users frequently express multiple intents within a single utterance. While there is an emerging interest in multi-intent detection (MID), existing in-domain datasets such as MixATIS and MixSNIPS have limitations in their formulation. To address these issues, we present BlendX, a suite of refined datasets featuring more diverse patterns than their predecessors, elevating both its complexity and diversity. For dataset construction, we utilize both rule-based heuristics as well as a generative tool—OpenAI’s ChatGPT—which is augmented with a similarity-driven strategy for utterance selection. To ensure the quality of the proposed datasets, we also introduce three novel metrics that assess the statistical properties of an utterance related to word count, conjunction use, and pronoun usage. Extensive experiments on BlendX reveal that state-of-the-art MID models struggle with the challenges posed by the new datasets, highlighting the need to reexamine the current state of the MID field. The dataset is available at https://github.com/HYU-NLP/BlendX.

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BLN600: A Parallel Corpus of Machine/Human Transcribed Nineteenth Century Newspaper Texts
Callum William Booth | Alan Thomas | Robert Gaizauskas

We present a publicly available corpus of nineteenth-century newspaper text focused on crime in London, derived from the Gale British Library Newspapers corpus parts 1 and 2. The corpus comprises 600 newspaper excerpts and for each excerpt contains the original source image, the machine transcription of that image as found in the BLN and a gold standard manual transcription that we have created. We envisage the corpus will be helpful for the training and development of OCR and post-OCR correction methodologies for historical newspaper machine transcription—for which there is currently a dearth of publicly available resources. In this paper, we discuss the rationale behind gathering such a corpus, the methodology used to select, process, and align the data, and the corpus’ potential utility for historians and digital humanities researchers—particularly within the realms of neural machine translation-based post-OCR correction approaches, and other natural language processing tasks that are critically affected by erroneous OCR.

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Bootstrapping UMR Annotations for Arapaho from Language Documentation Resources
Matthew J. Buchholz | Julia Bonn | Claire Benet Post | Andrew Cowell | Alexis Palmer

Uniform Meaning Representation (UMR) is a semantic labeling system in the AMR family designed to be uniformly applicable to typologically diverse languages. The UMR labeling system is quite thorough and can be time-consuming to execute, especially if annotators are starting from scratch. In this paper, we focus on methods for bootstrapping UMR annotations for a given language from existing resources, and specifically from typical products of language documentation work, such as lexical databases and interlinear glossed text (IGT). Using Arapaho as our test case, we present and evaluate a bootstrapping process that automatically generates UMR subgraphs from IGT. Additionally, we describe and evaluate a method for bootstrapping valency lexicon entries from lexical databases for both the target language and English. We are able to generate enough basic structure in UMR graphs from the existing Arapaho interlinearized texts to automate UMR labeling to a significant extent. Our method thus has the potential to streamline the process of building meaning representations for new languages without existing large-scale computational resources.

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BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses
Weihao Zeng | Keqing He | Yejie Wang | Dayuan Fu | Weiran Xu

Pre-trained language models have been successful in many scenarios. However, their usefulness in task-oriented dialogues is limited due to the intrinsic linguistic differences between general text and task-oriented dialogues. Current task-oriented dialogue pre-training methods rely on a contrastive framework, which faces challenges such as selecting true positives and hard negatives, as well as lacking diversity. In this paper, we propose a novel dialogue pre-training model called BootTOD. It learns task-oriented dialogue representations via a self-bootstrapping framework. Unlike contrastive counterparts, BootTOD aligns context and context+response representations and dismisses the requirements of contrastive pairs. BootTOD also uses multiple appropriate response targets to model the intrinsic one-to-many diversity of human conversations. Experimental results show that BootTOD outperforms strong TOD baselines on diverse downstream dialogue tasks.

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Born a BabyNet with Hierarchical Parental Supervision for End-to-End Text Image Machine Translation
Cong Ma | Yaping Zhang | Zhiyang Zhang | Yupu Liang | Yang Zhao | Yu Zhou | Chengqing Zong

Text image machine translation (TIMT) aims at translating source language texts in images into another target language, which has been proven successful by bridging text image recognition encoder and text translation decoder. However, it is still an open question of how to incorporate fine-grained knowledge supervision to make it consistent between recognition and translation modules. In this paper, we propose a novel TIMT method named as BabyNet, which is optimized with hierarchical parental supervision to improve translation performance. Inspired by genetic recombination and variation in the field of genetics, the proposed BabyNet is inherited from the recognition and translation parent models with a variation module of which parameters can be updated when training on the TIMT task. Meanwhile, hierarchical and multi-granularity supervision from parent models is introduced to bridge the gap between inherited modules in BabyNet. Extensive experiments on both synthetic and real-world TIMT tests show that our proposed method significantly outperforms existing methods. Further analyses of various parent model combinations show the good generalization of our method.

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BP4ER: Bootstrap Prompting for Explicit Reasoning in Medical Dialogue Generation
Yuhong He | Yongqi Zhang | Shizhu He | Jun Wan

Medical dialogue generation (MDG) has gained increasing attention due to its substantial practical value. Previous works typically employ a sequence-to-sequence framework to generate medical responses by modeling dialogue context as sequential text with annotated medical entities. While these methods have been successful in generating fluent responses, they fail to provide process explanations of reasoning and require extensive entity annotation. To address these limitations, we propose the method Bootstrap Prompting for Explicit Reasoning in MDG (BP4ER), which explicitly model MDG’s multi-step reasoning process and iteratively enhance this reasoning process. We employ a least-to-most prompting strategy to guide a large language model (LLM) in explicit reasoning, breaking down MDG into simpler sub-questions. These sub-questions build on answers from previous ones. Additionally, we also introduce two distinct bootstrapping techniques for prompting, which autonomously correct errors and facilitate the LLM’s explicit reasoning. This approach eliminates the need for entity annotation and increases the transparency of the MDG process by explicitly generating the intermediate reasoning chain. Experimental results on the two publicly datasets show that BP4ER outperforms state-of-the-art methods across both objective and subjective evaluation.

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Breakthrough from Nuance and Inconsistency: Enhancing Multimodal Sarcasm Detection with Context-Aware Self-Attention Fusion and Word Weight Calculation.
Hongfei Xue | Linyan Xu | Yu Tong | Rui Li | Jiali Lin | Dazhi Jiang

Multimodal sarcasm detection has received considerable attention due to its unique role in social networks. Existing methods often rely on feature concatenation to fuse different modalities or model the inconsistencies among modalities. However, sarcasm is often embodied in local and momentary nuances in a subtle way, which causes difficulty for sarcasm detection. To effectively incorporate these nuances, this paper presents Context-Aware Self-Attention Fusion (CAAF) to integrate local and momentary multimodal information into specific words. Furthermore, due to the instantaneous nature of sarcasm, the connotative meanings of words post-multimodal integration generally deviate from their denotative meanings. Therefore, Word Weight Calculation (WWC) is presented to compute the weight of specific words based on CAAF’s fusion nuances, illustrating the inconsistency between connotation and denotation. We evaluate our method on the MUStARD dataset, achieving an accuracy of 76.9 and an F1 score of 76.1, which surpasses the current state-of-the-art IWAN model by 1.7 and 1.6 respectively.

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Bridging Computational Lexicography and Corpus Linguistics: A Query Extension for OntoLex-FrAC
Christian Chiarcos | Ranka Stanković | Maxim Ionov | Gilles Sérasset

OntoLex, the dominant community standard for machine-readable lexical resources in the context of RDF, Linked Data and Semantic Web technologies, is currently extended with a designated module for Frequency, Attestations and Corpus-based Information (OntoLex-FrAC). We propose a novel component for OntoLex-FrAC, addressing the incorporation of corpus queries for (a) linking dictionaries with corpus engines, (b) enabling RDF-based web services to exchange corpus queries and responses data dynamically, and (c) using conventional query languages to formalize the internal structure of collocations, word sketches, and colligations. The primary field of application of the query extension is in digital lexicography and corpus linguistics, and we present a proof-of-principle implementation in backend components of a novel platform designed to support digital lexicography for the Serbian language.

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Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model
Shirin Dabbaghi Varnosfaderani | Canasai Kruengkrai | Ramin Yahyapour | Junichi Yamagishi

FEVEROUS is a benchmark and research initiative focused on fact extraction and verification tasks involving unstructured text and structured tabular data. In FEVEROUS, existing works often rely on extensive preprocessing and utilize rule-based transformations of data, leading to potential context loss or misleading encodings. This paper introduces a simple yet powerful model that nullifies the need for modality conversion, thereby preserving the original evidence’s context. By leveraging pre-trained models on diverse text and tabular datasets and by incorporating a lightweight attention-based mechanism, our approach efficiently exploits latent connections between different data types, thereby yielding comprehensive and reliable verdict predictions. The model’s modular structure adeptly manages multi-modal information, ensuring the integrity and authenticity of the original evidence are uncompromised. Comparative analyses reveal that our approach exhibits competitive performance, aligning itself closely with top-tier models on the FEVEROUS benchmark.

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Bridging the Code Gap: A Joint Learning Framework across Medical Coding Systems
Geunyeong Jeong | Seokwon Jeong | Juoh Sun | Harksoo Kim

Automated Medical Coding (AMC) is the task of automatically converting free-text medical documents into predefined codes according to a specific medical coding system. Although deep learning has significantly advanced AMC, the class imbalance problem remains a significant challenge. To address this issue, most existing methods consider only a single coding system and disregard the potential benefits of reflecting the relevance between different coding systems. To bridge this gap, we introduce a Joint learning framework for Across Medical coding Systems (JAMS), which jointly learns different coding systems through multi-task learning. It learns various representations using a shared encoder and explicitly captures the relationships across these coding systems using the medical code attention network, a modification of the graph attention network. In the experiments on the MIMIC-IV ICD-9 and MIMIC-IV ICD-10 datasets, connected through General Equivalence Mappings, JAMS improved the performance consistently regardless of the backbone models. This result demonstrates its model-agnostic characteristic, which is not constrained by specific model structures. Notably, JAMS significantly improved the performance of low-frequency codes. Our analysis shows that these performance gains are due to the connections between the codes of the different coding systems.

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Bring Invariant to Variant: A Contrastive Prompt-based Framework for Temporal Knowledge Graph Forecasting
Ying Zhang | Xinying Qian | Yu Zhao | Baohang Zhou | Kehui Song | Xiaojie Yuan

Temporal knowledge graph forecasting aims to reason over known facts to complete the missing links in the future. Existing methods are highly dependent on the structures of temporal knowledge graphs and commonly utilize recurrent or graph neural networks for forecasting. However, entities that are infrequently observed or have not been seen recently face challenges in learning effective knowledge representations due to insufficient structural contexts. To address the above disadvantages, in this paper, we propose a Contrastive Prompt-based framework with Entity background information for TKG forecasting, which we named CoPET. Specifically, to bring the time-invariant entity background information to time-variant structural information, we employ a dual encoder architecture consisting of a candidate encoder and a query encoder. A contrastive learning framework is used to encourage the query representation to be closer to the candidate representation. We further propose three kinds of trainable time-variant prompts aimed at capturing temporal structural information. Experiments on two datasets demonstrate that our method is effective and stays competitive in inference with limited structural information. Our code is available at https://github.com/qianxinying/CoPET.

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Building a Broad Infrastructure for Uniform Meaning Representations
Julia Bonn | Matthew J. Buchholz | Jayeol Chun | Andrew Cowell | William Croft | Lukas Denk | Sijia Ge | Jan Hajič | Kenneth Lai | James H. Martin | Skatje Myers | Alexis Palmer | Martha Palmer | Claire Benet Post | James Pustejovsky | Kristine Stenzel | Haibo Sun | Zdeňka Urešová | Rosa Vallejos | Jens E. L. Van Gysel | Meagan Vigus | Nianwen Xue | Jin Zhao

This paper reports the first release of the UMR (Uniform Meaning Representation) data set. UMR is a graph-based meaning representation formalism consisting of a sentence-level graph and a document-level graph. The sentence-level graph represents predicate-argument structures, named entities, word senses, aspectuality of events, as well as person and number information for entities. The document-level graph represents coreferential, temporal, and modal relations that go beyond sentence boundaries. UMR is designed to capture the commonalities and variations across languages and this is done through the use of a common set of abstract concepts, relations, and attributes as well as concrete concepts derived from words from invidual languages. This UMR release includes annotations for six languages (Arapaho, Chinese, English, Kukama, Navajo, Sanapana) that vary greatly in terms of their linguistic properties and resource availability. We also describe on-going efforts to enlarge this data set and extend it to other genres and modalities. We also briefly describe the available infrastructure (UMR annotation guidelines and tools) that others can use to create similar data sets.

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Building a Database of Conversational Routines
Polina Bychkova | Alyaxey Yaskevich | Serafima Gyulasaryan | Ekaterina Rakhilina

This paper discusses the Routinicon, a new constructicographic resource for the description of conversational routines. Conversational routines are defined as conventional formulaic expressions that language speakers use in standard extralinguistic situations (cf. Bless you! as a reaction to sneezing or Who’s there? as a typical answer to a knock on the door). The Routinicon’s goal is to accumulate the routines that constitute the inventory of conventional expressions in Russian language and systematically describe them in a way that would enable future cross-linguistic comparison and typological research. Conceptually, the Routinicon is a natural extension of such projects as the Russian Constructicon and Pragmaticon. It inherits their approach to the systematization of phraseological units as well as to the data collection. At the same time, the new project focuses on a fundamentally different domain of units and hence offers a radically new structure of linguistic annotation. Its principles and challenges are addressed in the paper.

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Building a Data Infrastructure for a Mid-Resource Language: The Case of Catalan
Aitor Gonzalez-Agirre | Montserrat Marimon | Carlos Rodriguez-Penagos | Javier Aula-Blasco | Irene Baucells | Carme Armentano-Oller | Jorge Palomar-Giner | Baybars Kulebi | Marta Villegas

Current LLM-based applications are becoming steadily available for everyone with a reliable access to technology and the internet. These applications offer benefits to their users that leave those without access to them at a serious disadvantage. Given the vastly large amount of data needed to train LLMs, the gap between languages with access to such quantity of data and those without it is currently larger than ever. Aimed at saving this gap, the Aina Project was created to provide Catalan with the necessary resources to keep being relevant in the context of AI/NLP applications based on LLMs. We thus present a set of strategies to consider when improving technology support for a mid- or low-resource language, specially addressing sustainability of high-quality data acquisition and the challenges involved in the process. We also introduce a large amount of new annotated data for Catalan. Our hope is that those interested in replicating this work for another language can learn from what worked for us, the challenges that we faced, and the sometimes disheartening truth of working with mid- and low-resource languages.

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Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer
Youmi Ma | An Wang | Naoaki Okazaki

Document-level Relation Extraction (DocRE) is the task of extracting all semantic relationships from a document. While studies have been conducted on English DocRE, limited attention has been given to DocRE in non-English languages. This work delves into effectively utilizing existing English resources to promote DocRE studies in non-English languages, with Japanese as the representative case. As an initial attempt, we construct a dataset by transferring an English dataset to Japanese. However, models trained on such a dataset are observed to suffer from low recalls. We investigate the error cases and attribute the failure to different surface structures and semantics of documents translated from English and those written by native speakers. We thus switch to explore if the transferred dataset can assist human annotation on Japanese documents. In our proposal, annotators edit relation predictions from a model trained on the transferred dataset. Quantitative analysis shows that relation recommendations suggested by the model help reduce approximately 50% of the human edit steps compared with the previous approach. Experiments quantify the performance of existing DocRE models on our collected dataset, portraying the challenges of Japanese and cross-lingual DocRE.

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Building MUSCLE, a Dataset for MUltilingual Semantic Classification of Links between Entities
Lucia Pitarch | Carlos Bobed Lisbona | David Abián | Jorge Gracia | Jordi Bernad

In this paper we introduce MUSCLE, a dataset for MUltilingual lexico-Semantic Classification of Links between Entities. The MUSCLE dataset was designed to train and evaluate Lexical Relation Classification (LRC) systems with 27K pairs of universal concepts selected from Wikidata, a large and highly multilingual factual Knowledge Graph (KG). Each pair of concepts includes its lexical forms in 25 languages and is labeled with up to five possible lexico-semantic relations between the concepts: hypernymy, hyponymy, meronymy, holonymy, and antonymy. Inspired by Semantic Map theory, the dataset bridges lexical and conceptual semantics, is more challenging and robust than previous datasets for LRC, avoids lexical memorization, is domain-balanced across entities, and enables enrichment and hierarchical information retrieval.

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Building Question-Answer Data Using Web Register Identification
Anni Eskelinen | Amanda Myntti | Erik Henriksson | Sampo Pyysalo | Veronika Laippala

This article introduces a resource-efficient method for developing question-answer (QA) datasets by extracting QA pairs from web-scale data using machine learning (ML). Our method benefits from recent advances in web register (genre) identification and consists of two ML steps with an additional post-processing step. First, using XLM-R and the multilingual CORE web register corpus series with categories such as QA Forum, we train a multilingual classifier to retrieve documents that are likely to contain QA pairs from web-scale data. Second, we develop a NER-style token classifier to identify the QA text spans within these documents. To this end, we experiment with training on a semi-synthetic dataset built on top of the English LFQA, a small set of manually cleaned web QA pairs in English and Finnish, and a Finnish web QA pair dataset cleaned using ChatGPT. The evaluation of our pipeline demonstrates its capability to efficiently retrieve a substantial volume of QA pairs. While the approach is adaptable to any language given the availability of language models and extensive web data, we showcase its efficiency in English and Finnish, developing the first open, non-synthetic and non-machine translated QA dataset for Finnish – Turku WebQA – comprising over 200,000 QA pairs.

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CAGK: Collaborative Aspect Graph Enhanced Knowledge-based Recommendation
Xiaotong Song | Huiping Lin | Jiatao Zhu | Xinyi Gong

Auxiliary information, such as knowledge graph (KG), has become increasingly crucial in recommender systems. However, the current KG-based recommendation still has some limitations: (1) low link rates between items and KG entities, (2) redundant knowledge in KG. In this paper, we introduce the aspect, which refers to keywords describing item attributes in reviews, to KG-based recommendation, and propose a new model, Collaborative Aspect Graph enhanced Knowledge-based Network (CAGK). Firstly, CAGK builds a Collaborative Aspect Graph (CAG) with user-item interactions, aspects and KG, where aspects can fill most of the sparsity. Secondly, we leverage interactive information and aspect features to generate aspect-aware guidance signals to customize knowledge extraction and eliminate redundant knowledge. Lastly, we utilize low ratings and negative aspect sentiment to capture features of that users dislike to prevent repetitive recommendations of disliked items. Experimental results on two widely used benchmark datasets, Amazon-book and Yelp2018, confirm the superiority of CAGK.

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CALAMR: Component ALignment for Abstract Meaning Representation
Paul Landes | Barbara Di Eugenio

We present Component ALignment for Abstract Meaning Representation (Calamr), a novel method for graph alignment that can support summarization and its evaluation. First, our method produces graphs that explain what is summarized through their alignments, which can be used to train graph based summarization learners. Second, although numerous scoring methods have been proposed for abstract meaning representation (AMR) that evaluate semantic similarity, no AMR based summarization metrics exist despite years of work using AMR for this task. Calamr provides alignments on which new scores can be based. The contributions of this work include a) a novel approach to aligning AMR graphs, b) a new summarization based scoring methods for similarity of AMR subgraphs composed of one or more sentences, and c) the entire reusable source code to reproduce our results.

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Calibrating LLM-Based Evaluator
Yuxuan Liu | Tianchi Yang | Shaohan Huang | Zihan Zhang | Haizhen Huang | Furu Wei | Weiwei Deng | Feng Sun | Qi Zhang

Recent advancements in large language models (LLMs) and their emergent capabilities make LLM a promising reference-free evaluator on the quality of natural language generation, and a competent alternative to human evaluation. However, hindered by the closed-source or high computational demand to host and tune, there is a lack of practice to further calibrate an off-the-shelf LLM-based evaluator towards better human alignment. In this work, we propose AutoCalibrate, a multi-stage, gradient-free approach to automatically calibrate and align an LLM-based evaluator toward human preference. Instead of explicitly modeling human preferences, we first implicitly encompass them within a set of human labels. Then, an initial set of scoring criteria is drafted by the language model itself, leveraging in-context learning on different few-shot examples. To further calibrate this set of criteria, we select the best performers and re-draft them with self-refinement. Our experiments on multiple text quality evaluation datasets illustrate a significant improvement in correlation with expert evaluation through calibration. Our comprehensive qualitative analysis conveys insightful intuitions and observations on the essence of effective scoring criteria.

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CAM 2.0: End-to-End Open Domain Comparative Question Answering System
Ahmad Shallouf | Hanna Herasimchyk | Mikhail Salnikov | Rudy Alexandro Garrido Veliz | Natia Mestvirishvili | Alexander Panchenko | Chris Biemann | Irina Nikishina

Comparative Question Answering (CompQA) is a Natural Language Processing task that combines Question Answering and Argument Mining approaches to answer subjective comparative questions in an efficient argumentative manner. In this paper, we present an end-to-end (full pipeline) system for answering comparative questions called CAM 2.0 as well as a public leaderboard called CompUGE that unifies the existing datasets under a single easy-to-use evaluation suite. As compared to previous web-form-based CompQA systems, it features question identification, object and aspect labeling, stance classification, and summarization using up-to-date models. We also select the most time- and memory-effective pipeline by comparing separately fine-tuned Transformer Encoder models which show state-of-the-art performance on the subtasks with Generative LLMs in few-shot and LoRA setups. We also conduct a user study for a whole-system evaluation.

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CAMAL: A Novel Dataset for Multi-label Conversational Argument Move Analysis
Viet Dac Lai | Duy Ngoc Pham | Jonathan Steinberg | Jamie Mikeska | Thien Huu Nguyen

Understanding the discussion moves that teachers and students use to engage in classroom discussions is important to support pre-service teacher learning and teacher educators. This work introduces a novel conversational multi-label corpus of teaching transcripts collected from a simulated classroom environment for Conversational Argument Move AnaLysis (CAMAL). The dataset offers various argumentation moves used by pre-service teachers and students in mathematics and science classroom discussions. The dataset includes 165 transcripts from these discussions that pre-service elementary teachers facilitated in a simulated classroom environment of five student avatars. The discussion transcripts were annotated by education assessment experts for nine argumentation moves (aka. intents) used by the pre-service teachers and students during the discussions. In this paper, we describe the dataset, our annotation framework, and the models we employed to detect argumentation moves. Our experiments with state-of-the-art models demonstrate the complexity of the CAMAL task presented in the dataset. The result reveals that models that combined CNN and LSTM structures with speaker ID graphs improved the F1-score of our baseline models to detect speakers’ intents by a large margin. Given the complexity of the CAMAL task, it creates research opportunities for future studies. We share the dataset, the source code, and the annotation framework publicly at http://github.com/uonlp/camal-dataset.

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Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic
Christian Khairallah | Salam Khalifa | Reham Marzouk | Mayar Nassar | Nizar Habash

We present Camel Morph MSA, the largest open-source Modern Standard Arabic morphological analyzer and generator. Camel Morph MSA has over 100K lemmas, and includes rarely modeled morphological features of Modern Standard Arabic with Classical Arabic origins. Camel Morph MSA can produce ∼1.45B analyses and ∼535M unique diacritizations, almost an order of magnitude larger than SAMA (Maamouri et al., 2010c), in addition to having ∼36% less OOV rate than SAMA on a 10B word corpus. Furthermore, Camel Morph MSA fills the gaps of many lemma paradigms by modeling linguistic phenomena consistently. Camel Morph MSA seamlessly integrates with the Camel Tools Python toolkit (Obeid et al., 2020), ensuring ease of use and accessibility.

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CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective Models on French Biomedical Data
Rian Touchent | Éric de la Clergerie

Clinical data in hospitals are increasingly accessible for research through clinical data warehouses. However these documents are unstructured and it is therefore necessary to extract information from medical reports to conduct clinical studies. Transfer learning with BERT-like models such as CamemBERT has allowed major advances for French, especially for named entity recognition. However, these models are trained for plain language and are less efficient on biomedical data. Addressing this gap, we introduce CamemBERT-bio, a dedicated French biomedical model derived from a new public French biomedical dataset. Through continual pre-training of the original CamemBERT, CamemBERT-bio achieves an improvement of 2.54 points of F1-score on average across various biomedical named entity recognition tasks, reinforcing the potential of continual pre-training as an equally proficient yet less computationally intensive alternative to training from scratch. Additionally, we highlight the importance of using a standard evaluation protocol that provides a clear view of the current state-of-the-art for French biomedical models.

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CAMERA³: An Evaluation Dataset for Controllable Ad Text Generation in Japanese
Go Inoue | Akihiko Kato | Masato Mita | Ukyo Honda | Peinan Zhang

Ad text generation is the task of creating compelling text from an advertising asset that describes products or services, such as a landing page. In advertising, diversity plays an important role in enhancing the effectiveness of an ad text, mitigating a phenomenon called “ad fatigue,” where users become disengaged due to repetitive exposure to the same advertisement. Despite numerous efforts in ad text generation, the aspect of diversifying ad texts has received limited attention, particularly in non-English languages like Japanese. To address this, we present CAMERA³, an evaluation dataset for controllable text generation in the advertising domain in Japanese. Our dataset includes 3,980 ad texts written by expert annotators, taking into account various aspects of ad appeals. We make CAMERA³ publicly available, allowing researchers to examine the capabilities of recent NLG models in controllable text generation in a real-world scenario.

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Can Factual Statements Be Deceptive? The DeFaBel Corpus of Belief-based Deception
Aswathy Velutharambath | Amelie Wührl | Roman Klinger

If a person firmly believes in a non-factual statement, such as “The Earth is flat”, and argues in its favor, there is no inherent intention to deceive. As the argumentation stems from genuine belief, it may be unlikely to exhibit the linguistic properties associated with deception or lying. This interplay of factuality, personal belief, and intent to deceive remains an understudied area. Disentangling the influence of these variables in argumentation is crucial to gain a better understanding of the linguistic properties attributed to each of them. To study the relation between deception and factuality, based on belief, we present the DeFaBel corpus, a crowd-sourced resource of belief-based deception. To create this corpus, we devise a study in which participants are instructed to write arguments supporting statements like “eating watermelon seeds can cause indigestion”, regardless of its factual accuracy or their personal beliefs about the statement. In addition to the generation task, we ask them to disclose their belief about the statement. The collected instances are labelled as deceptive if the arguments are in contradiction to the participants’ personal beliefs. Each instance in the corpus is thus annotated (or implicitly labelled) with personal beliefs of the author, factuality of the statement, and the intended deceptiveness. The DeFaBel corpus contains 1031 texts in German, out of which 643 are deceptive and 388 are non-deceptive. It is the first publicly available corpus for studying deception in German. In our analysis, we find that people are more confident in the persuasiveness of their arguments when the statement is aligned with their belief, but surprisingly less confident when they are generating arguments in favor of facts. The DeFaBel corpus can be obtained from https://www.ims.uni-stuttgart.de/data/defabel .

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Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles
Maram Hasanain | Fatema Ahmad | Firoj Alam

The use of propaganda has spiked on mainstream and social media, aiming to manipulate or mislead users. While efforts to automatically detect propaganda techniques in textual, visual, or multimodal content have increased, most of them primarily focus on English content. The majority of the recent initiatives targeting medium to low-resource languages produced relatively small annotated datasets, with a skewed distribution, posing challenges for the development of sophisticated propaganda detection models. To address this challenge, we carefully develop the largest propaganda dataset to date, ArPro, comprised of 8K paragraphs from newspaper articles, labeled at the text span level following a taxonomy of 23 propagandistic techniques. Furthermore, our work offers the first attempt to understand the performance of large language models (LLMs), using GPT-4, for fine-grained propaganda detection from text. Results showed that GPT-4’s performance degrades as the task moves from simply classifying a paragraph as propagandistic or not, to the fine-grained task of detecting propaganda techniques and their manifestation in text. Compared to models fine-tuned on the dataset for propaganda detection at different classification granularities, GPT-4 is still far behind. Finally, we evaluate GPT-4 on a dataset consisting of six other languages for span detection, and results suggest that the model struggles with the task across languages. We made the dataset publicly available for the community.

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Can Humans Identify Domains?
Maria Barrett | Max Müller-Eberstein | Elisa Bassignana | Amalie Brogaard Pauli | Mike Zhang | Rob van der Goot

Textual domain is a crucial property within the Natural Language Processing (NLP) community due to its effects on downstream model performance. The concept itself is, however, loosely defined and, in practice, refers to any non-typological property, such as genre, topic, medium or style of a document. We investigate the core notion of domains via human proficiency in identifying related intrinsic textual properties, specifically the concepts of genre (communicative purpose) and topic (subject matter). We publish our annotations in TGeGUM: A collection of 9.1k sentences from the GUM dataset (Zeldes, 2017) with single sentence and larger context (i.e., prose) annotations for one of 11 genres (source type), and its topic/subtopic as per the Dewey Decimal library classification system (Dewey, 1979), consisting of 10/100 hierarchical topics of increased granularity. Each instance is annotated by three annotators, for a total of 32.7k annotations, allowing us to examine the level of human disagreement and the relative difficulty of each annotation task. With a Fleiss’ kappa of at most 0.53 on the sentence level and 0.66 at the prose level, it is evident that despite the ubiquity of domains in NLP, there is little human consensus on how to define them. By training classifiers to perform the same task, we find that this uncertainty also extends to NLP models.

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Can Language Models Learn Embeddings of Propositional Logic Assertions?
Nurul Fajrin Ariyani | Zied Bouraoui | Richard Booth | Steven Schockaert

Natural language offers an appealing alternative to formal logics as a vehicle for representing knowledge. However, using natural language means that standard methods for automated reasoning can no longer be used. A popular solution is to use transformer-based language models (LMs) to directly reason about knowledge expressed in natural language, but this has two important limitations. First, the set of premises is often too large to be directly processed by the LM. This means that we need a retrieval strategy which can select the most relevant premises when trying to infer some conclusion. Second, LMs have been found to learn shortcuts and thus lack robustness, putting in doubt to what extent they actually understand the knowledge that is expressed. Given these limitations, we explore the following alternative: rather than using LMs to perform reasoning directly, we use them to learn embeddings of individual assertions. Reasoning is then carried out by manipulating the learned embeddings. We show that this strategy is feasible to some extent, while at the same time also highlighting the limitations of directly fine-tuning LMs to learn the required embeddings.

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Can Large Language Models Automatically Score Proficiency of Written Essays?
Watheq Ahmad Mansour | Salam Albatarni | Sohaila Eltanbouly | Tamer Elsayed

Although several methods were proposed to address the problem of automated essay scoring (AES) in the last 50 years, there is still much to desire in terms of effectiveness. Large Language Models (LLMs) are transformer-based models that demonstrate extraordinary capabilities on various tasks. In this paper, we test the ability of LLMs, given their powerful linguistic knowledge, to analyze and effectively score written essays. We experimented with two popular LLMs, namely ChatGPT and Llama. We aim to check if these models can do this task and, if so, how their performance is positioned among the state-of-the-art (SOTA) models across two levels, holistically and per individual writing trait. We utilized prompt-engineering tactics in designing four different prompts to bring their maximum potential on this task. Our experiments conducted on the ASAP dataset revealed several interesting observations. First, choosing the right prompt depends highly on the model and nature of the task. Second, the two LLMs exhibited comparable average performance in AES, with a slight advantage for ChatGPT. Finally, despite the performance gap between the two LLMs and SOTA models in terms of predictions, they provide feedback to enhance the quality of the essays, which can potentially help both teachers and students.

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Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences
Sai Koneru | Jian Wu | Sarah Rajtmajer

Hypothesis formulation and testing are central to empirical research. A strong hypothesis is a best guess based on existing evidence and informed by a comprehensive view of relevant literature. However, with exponential increase in the number of scientific articles published annually, manual aggregation and synthesis of evidence related to a given hypothesis is a challenge. Our work explores the ability of current large language models (LLMs) to discern evidence in support or refute of specific hypotheses based on the text of scientific abstracts. We share a novel dataset for the task of scientific hypothesis evidencing using community-driven annotations of studies in the social sciences. We compare the performance of LLMs to several state of the art methods and highlight opportunities for future research in this area. Our dataset is shared with the research community: https://github.com/Sai90000/ScientificHypothesisEvidencing.git

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Can Large Language Models Learn Translation Robustness from Noisy-Source In-context Demonstrations?
Leiyu Pan | Yongqi Leng | Deyi Xiong

Large language models (LLMs) have been used for machine translation. When provided with prompts and source sentences, LLMs can achieve impressive translation results. However, the robustness of these LLMs remains a significant challenge, as they often struggle to accurately translate sentences in the presence of noise, even when using similarity-based in-context learning methods. This work proposes a research scheme for studying machine translation robustness on LLMs, investigating whether LLMs can learn translation robustness from noisy-source demonstration examples. Through experiments on different models, languages, and noise types, we empirically demonstrate that LLMs can learn how to handle noise and translation methods from noisy-source demonstration examples, thereby improving their translation performance on noisy sentences. Furthermore, we find that increasing the noise ratio appropriately for the noisy-source demonstration examples can enhance the translation robustness of LLMs. Additionally, we also attempt to investigate scenarios where LLMs are more likely to learn translation robustness for mixed and specific types of noise. We find that the model’s performance varies across different noise settings.

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Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?
Shaoxiong Ji | Timothee Mickus | Vincent Segonne | Jörg Tiedemann

Multilingual pretraining and fine-tuning have remarkably succeeded in various natural language processing tasks. Transferring representations from one language to another is especially crucial for cross-lingual learning. One can expect machine translation objectives to be well suited to fostering such capabilities, as they involve the explicit alignment of semantically equivalent sentences from different languages. This paper investigates the potential benefits of employing machine translation as a continued training objective to enhance language representation learning, bridging multilingual pretraining and cross-lingual applications. We study this question through two lenses: a quantitative evaluation of the performance of existing models and an analysis of their latent representations. Our results show that, contrary to expectations, machine translation as the continued training fails to enhance cross-lingual representation learning in multiple cross-lingual natural language understanding tasks. We conclude that explicit sentence-level alignment in the cross-lingual scenario is detrimental to cross-lingual transfer pretraining, which has important implications for future cross-lingual transfer studies. We furthermore provide evidence through similarity measures and investigation of parameters that this lack of positive influence is due to output separability—which we argue is of use for machine translation but detrimental elsewhere.

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Can Multiple-choice Questions Really Be Useful in Detecting the Abilities of LLMs?
Wangyue Li | Liangzhi Li | Tong Xiang | Xiao Liu | Wei Deng | Noa Garcia

Multiple-choice questions (MCQs) are widely used in the evaluation of large language models (LLMs) due to their simplicity and efficiency. However, there are concerns about whether MCQs can truly measure LLM’s capabilities, particularly in knowledge-intensive scenarios where long-form generation (LFG) answers are required. The misalignment between the task and the evaluation method demands a thoughtful analysis of MCQ’s efficacy, which we undertake in this paper by evaluating nine LLMs on four question-answering (QA) datasets in two languages: Chinese and English. We identify a significant issue: LLMs exhibit an order sensitivity in bilingual MCQs, favoring answers located at specific positions, i.e., the first position. We further quantify the gap between MCQs and long-form generation questions (LFGQs) by comparing their direct outputs, token logits, and embeddings. Our results reveal a relatively low correlation between answers from MCQs and LFGQs for identical questions. Additionally, we propose two methods to quantify the consistency and confidence of LLMs’ output, which can be generalized to other QA evaluation benchmarks. Notably, our analysis challenges the idea that the higher the consistency, the greater the accuracy. We also find MCQs to be less reliable than LFGQs in terms of expected calibration error. Finally, the misalignment between MCQs and LFGQs is not only reflected in the evaluation performance but also in the embedding space. Our code and models can be accessed at https://github.com/Meetyou-AI-Lab/Can-MC-Evaluate-LLMs.

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Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought
Jooyoung Lee | Fan Yang | Thanh Tran | Qian Hu | Emre Barut | Kai-Wei Chang

We introduce a novel framework, LM-Guided CoT, that leverages a lightweight (i.e., <1B) language model (LM) for guiding a black-box large (i.e., >10B) LM in reasoning tasks. Specifically, the lightweight LM first generates a rationale for each input instance. The Frozen large LM is then prompted to predict a task output based on the rationale generated by the lightweight LM. Our approach is resource-efficient in the sense that it only requires training the lightweight LM. We optimize the model through 1) knowledge distillation and 2) reinforcement learning from rationale-oriented and task-oriented reward signals. We assess our method with multi-hop extractive question answering (QA) benchmarks, HotpotQA, and 2WikiMultiHopQA. Experimental results show that our approach outperforms all baselines regarding answer prediction accuracy. We also find that reinforcement learning helps the model to produce higher-quality rationales with improved QA performance.

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Can We Identify Stance without Target Arguments? A Study for Rumour Stance Classification
Yue Li | Carolina Scarton

Considering a conversation thread, rumour stance classification aims to identify the opinion (e.g. agree or disagree) of replies towards a target (rumour story). Although the target is expected to be an essential component in traditional stance classification, we show that rumour stance classification datasets contain a considerable amount of real-world data whose stance could be naturally inferred directly from the replies, contributing to the strong performance of the supervised models without awareness of the target. We find that current target-aware models underperform in cases where the context of the target is crucial. Finally, we propose a simple yet effective framework to enhance reasoning with the targets, achieving state-of-the-art performance on two benchmark datasets.

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Can We Learn Question, Answer, and Distractors All from an Image? A New Task for Multiple-choice Visual Question Answering
Wenjian Ding | Yao Zhang | Jun Wang | Adam Jatowt | Zhenglu Yang

Multiple-choice visual question answering (MC VQA) requires an answer picked from a list of distractors, based on a question and an image. This research has attracted wide interest from the fields of visual question answering, visual question generation, and visual distractor generation. However, these fields still stay in their own territories, and how to jointly generate meaningful questions, correct answers, and challenging distractors remains unexplored. In this paper, we introduce a novel task, Visual Question-Answer-Distractors Generation (VQADG), which can bridge this research gap as well as take as a cornerstone to promote existing VQA models. Specific to the VQADG task, we present a novel framework consisting of a vision-and-language model to encode the given image and generate QADs jointly, and contrastive learning to ensure the consistency of the generated question, answer, and distractors. Empirical evaluations on the benchmark dataset validate the performance of our model in the VQADG task.

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CARE: Co-Attention Network for Joint Entity and Relation Extraction
Wenjun Kong | Yamei Xia

Joint entity and relation extraction is the fundamental task of information extraction, consisting of two subtasks: named entity recognition and relation extraction. However, most existing joint extraction methods suffer from issues of feature confusion or inadequate interaction between the two subtasks. Addressing these challenges, in this work, we propose a Co-Attention network for joint entity and Relation Extraction (CARE). Our approach includes adopting a parallel encoding strategy to learn separate representations for each subtask, aiming to avoid feature overlap or confusion. At the core of our approach is the co-attention module that captures two-way interaction between the two subtasks, allowing the model to leverage entity information for relation prediction and vice versa, thus promoting mutual enhancement. Through extensive experiments on three benchmark datasets for joint entity and relation extraction (NYT, WebNLG, and SciERC), we demonstrate that our proposed model outperforms existing baseline models. Our code will be available at https://github.com/kwj0x7f/CARE.

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CareCorpus: A Corpus of Real-World Solution-Focused Caregiver Strategies for Personalized Pediatric Rehabilitation Service Design
Mina Valizadeh | Vera C. Kaelin | Mary A. Khetani | Natalie Parde

In pediatric rehabilitation services, one intervention approach involves using solution-focused caregiver strategies to support children in their daily life activities. The manual sharing of these strategies is not scalable, warranting need for an automated approach to recognize and select relevant strategies. We introduce CareCorpus, a dataset of 780 real-world strategies written by caregivers. Strategies underwent dual-annotation by three trained annotators according to four established rehabilitation classes (i.e., environment/context, n=325 strategies; a child’s sense of self, n=151 strategies; a child’s preferences, n=104 strategies; and a child’s activity competences, n=62 strategies) and a no-strategy class (n=138 instances) for irrelevant or indeterminate instances. The average percent agreement was 80.18%, with a Cohen’s Kappa of 0.75 across all classes. To validate this dataset, we propose multi-grained classification tasks for detecting and categorizing strategies, and establish new performance benchmarks ranging from F1=0.53-0.79. Our results provide a first step towards a smart option to sort caregiver strategies for use in designing pediatric rehabilitation care plans. This novel, interdisciplinary resource and application is also anticipated to generalize to other pediatric rehabilitation service contexts that target children with developmental need.

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CASIMIR: A Corpus of Scientific Articles Enhanced with Multiple Author-Integrated Revisions
Léane Isabelle Jourdan | Florian Boudin | Nicolas Hernandez | Richard Dufour

Writing a scientific article is a challenging task as it is a highly codified and specific genre, consequently proficiency in written communication is essential for effectively conveying research findings and ideas. In this article, we propose an original textual resource on the revision step of the writing process of scientific articles. This new dataset, called CASIMIR, contains the multiple revised versions of 15,646 scientific articles from OpenReview, along with their peer reviews. Pairs of consecutive versions of an article are aligned at sentence-level while keeping paragraph location information as metadata for supporting future revision studies at the discourse level. Each pair of revised sentences is enriched with automatically extracted edits and associated revision intention. To assess the initial quality on the dataset, we conducted a qualitative study of several state-of-the-art text revision approaches and compared various evaluation metrics. Our experiments led us to question the relevance of the current evaluation methods for the text revision task.

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Categorial Grammar Induction with Stochastic Category Selection
Christian Clark | William Schuler

Grammar induction, the task of learning a set of syntactic rules from minimally annotated training data, provides a means of exploring the longstanding question of whether humans rely on innate knowledge to acquire language. Of the various formalisms available for grammar induction, categorial grammars provide an appealing option due to their transparent interface between syntax and semantics. However, to obtain competitive results, previous categorial grammar inducers have relied on shortcuts such as part-of-speech annotations or an ad hoc bias term in the objective function to ensure desirable branching behavior. We present a categorial grammar inducer that eliminates both shortcuts: it learns from raw data, and does not rely on a biased objective function. This improvement is achieved through a novel stochastic process used to select the set of available syntactic categories. On a corpus of English child-directed speech, the model attains a recall-homogeneity of 0.48, a large improvement over previous categorial grammar inducers.

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Causal Intersectionality and Dual Form of Gradient Descent for Multimodal Analysis: A Case Study on Hateful Memes
Yosuke Miyanishi | Minh Le Nguyen

Amidst the rapid expansion of Machine Learning (ML) and Large Language Models (LLMs), understanding the semantics within their mechanisms is vital. Causal analyses define semantics, while gradient-based methods are essential to eXplainable AI (XAI), interpreting the model’s ‘black box’. Integrating these, we investigate how a model’s mechanisms reveal its causal effect on evidence-based decision-making. Research indicates intersectionality - the combined impact of an individual’s demographics - can be framed as an Average Treatment Effect (ATE). This paper demonstrates that hateful meme detection can be viewed as an ATE estimation using intersectionality principles, and summarized gradient-based attention scores highlight distinct behaviors of three Transformer models. We further reveal that LLM Llama-2 can discern the intersectional aspects of the detection through in-context learning and that the learning process could be explained via meta-gradient, a secondary form of gradient. In conclusion, this work furthers the dialogue on Causality and XAI. Our code is available online (see External Resources section).

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CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models
Yufei Huang | Deyi Xiong

Holistically measuring societal biases of large language models is crucial for detecting and reducing ethical risks in highly capable AI models. In this work, we present a Chinese Bias Benchmark dataset that consists of over 100K questions jointly constructed by human experts and generative language models, covering stereotypes and societal biases in 14 social dimensions related to Chinese culture and values. The curation process contains 4 essential steps: bias identification, ambiguous context generation, AI-assisted disambiguous context generation, and manual review and recomposition. The testing instances in the dataset are automatically derived from 3K+ high-quality templates manually authored with stringent quality control. The dataset exhibits wide coverage and high diversity. Extensive experiments demonstrate the effectiveness of the dataset in evaluating model bias, with all 12 publicly available Chinese large language models exhibiting strong bias in certain categories. Additionally, we observe from our experiments that fine-tuned models could, to a certain extent, heed instructions and avoid generating harmful outputs, in the way of “moral self-correction”. Our dataset is available at https://anonymous.4open.science/r/CBBQ-B860/.

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CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering
Hongbin Na

The recent advancements in artificial intelligence highlight the potential of language models in psychological health support. While models trained on data from mental health service platform have achieved preliminary success, challenges persist in areas such as data scarcity, quality, and ensuring a solid foundation in psychological techniques. To address these challenges, this study introduces a novel approach to enhance the precision and efficacy of psychological support through large language models. Specifically, we design a specific prompt derived from principles of Cognitive Behavioral Therapy (CBT) and have generated the CBT QA dataset, specifically for Chinese psychological health Q&A based on CBT structured intervention strategies. Unlike previous methods, our dataset emphasizes professional and structured response. Utilizing this dataset, we fine-tuned the large language model, giving birth to CBT-LLM, the large-scale language model specifically designed for Cognitive Behavioral Therapy techniques. Empirical evaluations demonstrate that CBT-LLM excels in generating structured, professional, and highly relevant responses in psychological health support tasks, showcasing its practicality and quality. The model is available on Hugging Face: https://huggingface.co/Hongbin37/CBT-LLM.

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CB-Whisper: Contextual Biasing Whisper Using Open-Vocabulary Keyword-Spotting
Yuang Li | Yinglu Li | Min Zhang | Chang Su | Jiawei Yu | Mengyao Piao | Xiaosong Qiao | Miaomiao Ma | Yanqing Zhao | Hao Yang

End-to-end automatic speech recognition (ASR) systems often struggle to recognize rare name entities, such as personal names, organizations and terminologies that are not frequently encountered in the training data. This paper presents Contextual Biasing Whisper (CB-Whisper), a novel ASR system based on OpenAI’s Whisper model that can recognize user-defined name entities by performing open-vocabulary keyword-spotting (KWS) before the decoder. The KWS module leverages text-to-speech (TTS) techniques and a convolutional neural network (CNN) classifier to match the features between the entities and the utterances. To integrate the recognized entities into the Whipser decoder and avoid hallucinations, we carefully crafted multiple prompts with spoken form hints. Experiments show that the KWS module based on Whisper encoder’s features can recognize unseen user-defined keywords effectively. More importantly, the proposed CB-Whisper substantially improves the mixed-error-rate (MER) and entity recall compared to the original Whisper model on three internal datasets and two publicly available datasets including Aishell and ACL datasets that cover English-only, Chinese-only, and code-switching scenarios.

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CEPT: A Contrast-Enhanced Prompt-Tuning Framework for Emotion Recognition in Conversation
Qingqing Gao | Jiuxin Cao | Biwei Cao | Xin Guan | Bo Liu

Emotion Recognition in Conversation (ERC) has attracted increasing attention due to its wide applications in public opinion analysis, empathetic conversation generation, and so on. However, ERC research suffers from the problems of data imbalance and the presence of similar linguistic expressions for different emotions. These issues can result in limited learning for minority emotions, biased predictions for common emotions, and the misclassification of different emotions with similar linguistic expressions. To alleviate these problems, we propose a Contrast-Enhanced Prompt-Tuning (CEPT) framework for ERC. We transform the ERC task into a Masked Language Modeling (MLM) generation task and generate the emotion for each utterance in the conversation based on the prompt-tuning of the Pre-trained Language Model (PLM), where a novel mixed prompt template and a label mapping strategy are introduced for better context and emotion feature modeling. Moreover, Supervised Contrastive Learning (SCL) is employed to help the PLM mine more information from the labels and learn a more discriminative representation space for utterances with different emotions. We conduct extensive experiments and the results demonstrate that CEPT outperforms the state-of-the-art methods on all three benchmark datasets and excels in recognizing minority emotions.

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CE-VDG: Counterfactual Entropy-based Bias Reduction for Video-grounded Dialogue Generation
Hongcheng Liu | Pingjie Wang | Zhiyuan Zhu | Yanfeng Wang | Yu Wang

The Video-Grounded Dialogue generation (VDG) is a challenging task requiring a comprehensive understanding of the multi-modal information to produce a pertinent response. However, VDG models may rely on dataset bias as a shortcut and fail to learn the multi-modal knowledge from both video and audio. Counterfactual reasoning is an effective method that can estimate and eliminate bias on some special aspects of classification tasks. However, conventional counterfactual reasoning cannot be applied to VDG tasks directly due to the BPE algorithm. In this paper, we reformulate the counterfactual reasoning from the information entropy perspective and extend it from the classification task to the generative task, which can effectively reduce the question-related bias in the auto-regressive generation task. We design CE-VDG to demonstrate the effectiveness in bias elimination of the reformulated counterfactual reasoning by using the proposed counterfactual entropy as an external loss. Extensive experiment results on two popular VDG datasets show the superiority of CE-VDG over the existing baseline method, demonstrating the effective debiasing capability in our model considering counterfactual entropy.

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ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting
Xiaoxue Cheng | Junyi Li | Wayne Xin Zhao | Ji-Rong Wen

Chain-of-Thought (CoT) prompting can enhance the reasoning capabilities of large language models (LLMs), establishing itself as a primary approach to solving complex reasoning tasks. Existing CoT synthesis approaches usually focus on simpler reasoning tasks and thus result in low-quality and inconsistent CoT prompts. In response to this challenge, we present an empirical investigation of CoT prompting and introduce CoTGenius, a novel framework designed for the automatic generation of superior CoT prompts. CoTGenius is developed based on three major evolution strategies, i.e., complicate, diversify, and specify—alongside two filtering mechanisms: evolutionary success judgement and correctness verification. We further employ CoTGenius to create an extensive CoT dataset, and subsequently fine-tune the Llama 2-Chat 7B and 13B models on this dataset. We call the resulting model ChainLM. To deal with the cumulative error issue in reasoning steps, we propose a step-level debating method, wherein multiple debaters discuss each reasoning step to arrive at the correct answer. Extensive experiments demonstrate that our ChainLM models exhibit enhanced proficiency in addressing a spectrum of complex reasoning problems compared to existing models. In addition, we conduct an in-depth analysis of the impact of data categories within CoTGenius on the model performance. We release our dataset and code at https://github.com/RUCAIBox/ChainLM.

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ChainNet: Structured Metaphor and Metonymy in WordNet
Rowan Hall Maudslay | Simone Teufel | Francis Bond | James Pustejovsky

The senses of a word exhibit rich internal structure. In a typical lexicon, this structure is overlooked: A word’s senses are encoded as a list, without inter-sense relations. We present ChainNet, a lexical resource which for the first time explicitly identifies these structures, by expressing how senses in the Open English Wordnet are derived from one another. In ChainNet, every nominal sense of a word is either connected to another sense by metaphor or metonymy, or is disconnected (in the case of homonymy). Because WordNet senses are linked to resources which capture information about their meaning, ChainNet represents the first dataset of grounded metaphor and metonymy.

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Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitations
Gregor Donabauer | Udo Kruschwitz

Pre-training of neural networks has recently revolutionized the field of Natural Language Processing (NLP) and has before demonstrated its effectiveness in computer vision. At the same time, advances around the detection of fake news were mainly driven by the context-based paradigm, where different types of signals (e.g. from social media) form graph-like structures that hold contextual information apart from the news article to classify. We propose to merge these two developments by applying pre-training of Graph Neural Networks (GNNs) in the domain of context-based fake news detection. Our experiments provide an evaluation of different pre-training strategies for graph-based misinformation detection and demonstrate that transfer learning does currently not lead to significant improvements over training a model from scratch in the domain. We argue that a major current issue is the lack of suitable large-scale resources that can be used for pre-training.

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Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes
Isar Nejadgholi | Kathleen C. Fraser | Anna Kerkhof | Svetlana Kiritchenko

Gender stereotypes are pervasive beliefs about individuals based on their gender that play a significant role in shaping societal attitudes, behaviours, and even opportunities. Recognizing the negative implications of gender stereotypes, particularly in online communications, this study investigates eleven strategies to automatically counteract and challenge these views. We present AI-generated gender-based counter-stereotypes to (self-identified) male and female study participants and ask them to assess their offensiveness, plausibility, and potential effectiveness. The strategies of counter-facts and broadening universals (i.e., stating that anyone can have a trait regardless of group membership) emerged as the most robust approaches, while humour, perspective-taking, counter-examples, and empathy for the speaker were perceived as less effective. Also, the differences in ratings were more pronounced for stereotypes about the different targets than between the genders of the raters. Alarmingly, many AI-generated counter-stereotypes were perceived as offensive and/or implausible. Our analysis and the collected dataset offer foundational insight into counter-stereotype generation, guiding future efforts to develop strategies that effectively challenge gender stereotypes in online interactions.

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Characteristic AI Agents via Large Language Models
Xi Wang | Hongliang Dai | Shen Gao | Piji Li

The advancement of Large Language Models (LLMs) has led to significant enhancements in the performance of chatbot systems. Many researchers have dedicated their efforts to the development of bringing characteristics to chatbots. While there have been commercial products for developing role-driven chatbots using LLMs, it is worth noting that academic research in this area remains relatively scarce. Our research focuses on investigating the performance of LLMs in constructing Characteristic AI Agents by simulating real-life individuals across different settings. Current investigations have primarily focused on act on roles with simple profiles. In response to this research gap, we create a benchmark for the characteristic AI agents task, including dataset, techniques, and evaluation metrics. A dataset called “Character100” is built for this benchmark, comprising the most-visited people on Wikipedia for language models to role-play. With the constructed dataset, we conduct comprehensive assessment of LLMs across various settings. In addition, we devise a set of automatic metrics for quantitative performance evaluation. The experimental results underscore the potential directions for further improvement in the capabilities of LLMs in constructing characteristic AI agents. The benchmark is available at https://github.com/nuaa-nlp/Character100.

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Character-level Language Models for Abbreviation and Long-form Detection
Leonardo Zilio | Shenbin Qian | Diptesh Kanojia | Constantin Orasan

Abbreviations and their associated long forms are important textual elements that are present in almost every scientific communication, and having information about these forms can help improve several NLP tasks. In this paper, our aim is to fine-tune language models for automatically identifying abbreviations and long forms. We used existing datasets which are annotated with abbreviations and long forms to train and test several language models, including transformer models, character-level language models, stacking of different embeddings, and ensemble methods. Our experiments showed that it was possible to achieve state-of-the-art results by stacking RoBERTa embeddings with domain-specific embeddings. However, the analysis of our first run showed that one of the datasets had issues in the BIO annotation, which led us to propose a revised dataset. After re-training selected models on the revised dataset, results show that character-level models achieve comparable results, especially when detecting abbreviations, but both RoBERTa large and the stacking of embeddings presented better results on biomedical data. When tested on a different subdomain (segments extracted from computer science texts), an ensemble method proved to yield the best results for the detection of long forms, and a character-level model had the best performance in detecting abbreviations.

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Charles Translator: A Machine Translation System between Ukrainian and Czech
Martin Popel | Lucie Polakova | Michal Novák | Jindřich Helcl | Jindřich Libovický | Pavel Straňák | Tomas Krabac | Jaroslava Hlavacova | Mariia Anisimova | Tereza Chlanova

We present Charles Translator, a machine translation system between Ukrainian and Czech, developed as part of a society-wide effort to mitigate the impact of the Russian-Ukrainian war on individuals and society. The system was developed in the spring of 2022 with the help of many language data providers in order to quickly meet the demand for such a service, which was not available at the time in the required quality. The translator was later implemented as an online web interface and as an Android app with speech input, both featuring Cyrillic-Latin script transliteration. The system translates directly, in comparison to other available systems that use English as a pivot, and thus makes advantage of the typological similarity of the two languages. It uses the block back-translation method which allows for efficient use of monolingual training data. The paper describes the development process including data collection and implementation, evaluation, mentions several use cases and outlines possibilities for further development of the system for educational purposes.

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Charting the Linguistic Landscape of Developing Writers: An Annotation Scheme for Enhancing Native Language Proficiency
Miguel Da Corte | Jorge Baptista

This study describes a pilot annotation task designed to capture orthographic, grammatical, lexical, semantic, and discursive patterns exhibited by college native English speakers participating in developmental education (DevEd) courses. The paper introduces an annotation scheme developed by two linguists aiming at pinpointing linguistic challenges that hinder effective written communication. The scheme builds upon patterns supported by the literature, which are known as predictors of student placement in DevEd courses and English proficiency levels. Other novel, multilayered, linguistic aspects that the literature has not yet explored are also presented. The scheme and its primary categories are succinctly presented and justified. Two trained annotators used this scheme to annotate a sample of 103 text units (3 during the training phase and 100 during the annotation task proper). Texts were randomly selected from a population of 290 community college intending students. An in-depth quality assurance inspection was conducted to assess tagging consistency between annotators and to discern (and address) annotation inaccuracies. Krippendorff’s Alpha (K-alpha) interrater reliability coefficients were calculated, revealing a K-alpha score of k=0.40, which corresponds to a moderate level of agreement, deemed adequate for the complexity and length of the annotation task.

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ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization
Mengsha Liu | Daoyuan Chen | Yaliang Li | Guian Fang | Ying Shen

Data visualization serves as a critical means for presenting data and mining its valuable insights. The task of chart summarization, through natural language processing techniques, facilitates in-depth data analysis of charts. However, there still are notable deficiencies in terms of visual-language matching and reasoning ability for existing approaches. To address these limitations, this study constructs a large-scale dataset of comprehensive chart-caption pairs and fine-tuning instructions on each chart. Thanks to the broad coverage of various topics and visual styles within this dataset, better matching degree can be achieved from the view of training data. Moreover, we propose an innovative chart summarization method, ChartThinker, which synthesizes deep analysis based on chains of thought and strategies of context retrieval, aiming to improve the logical coherence and accuracy of the generated summaries. Built upon the curated datasets, our trained model consistently exhibits superior performance in chart summarization tasks, surpassing 8 state-of-the-art models over 7 evaluation metrics. Our dataset and codes are publicly accessible.

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ChatASU: Evoking LLM’s Reflexion to Truly Understand Aspect Sentiment in Dialogues
Yiding Liu | Jingjing Wang | Jiamin Luo | Tao Zeng | Guodong Zhou

Aspect Sentiment Understanding (ASU) in interactive scenarios (e.g., Question-Answering and Dialogue) has attracted ever-more interest in recent years and achieved important progresses. However, existing studies on interactive ASU largely ignore the coreference issue for opinion targets (i.e., aspects), while this phenomenon is ubiquitous in interactive scenarios especially dialogues, limiting the ASU performance. Recently, large language models (LLMs) shows the powerful ability to integrate various NLP tasks with the chat paradigm. In this way, this paper proposes a new Chat-based Aspect Sentiment Understanding (ChatASU) task, aiming to explore LLMs’ ability in understanding aspect sentiments in dialogue scenarios. Particularly, this ChatASU task introduces a sub-task, i.e., Aspect Chain Reasoning (ACR) task, to address the aspect coreference issue. On this basis, we propose a Trusted Self-reflexion Approach (TSA) with ChatGLM as backbone to ChatASU. Specifically, this TSA treats the ACR task as an auxiliary task to boost the performance of the primary ASU task, and further integrates trusted learning into reflexion mechanisms to alleviate the LLMs-intrinsic factual hallucination problem in TSA. Furthermore, a high-quality ChatASU dataset is annotated to evaluate TSA, and extensive experiments show that our proposed TSA can significantly outperform several state-of-the-art baselines, justifying the effectiveness of TSA to ChatASU and the importance of considering the coreference and hallucination issues in ChatASU.

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ChatEL: Entity Linking with Chatbots
Yifan Ding | Qingkai Zeng | Tim Weninger

Entity Linking (EL) is an essential and challenging task in natural language processing that seeks to link some text representing an entity within a document or sentence with its corresponding entry in a dictionary or knowledge base. Most existing approaches focus on creating elaborate contextual models that look for clues the words surrounding the entity-text to help solve the linking problem. Although these fine-tuned language models tend to work, they can be unwieldy, difficult to train, and do not transfer well to other domains. Fortunately, Large Language Models (LLMs) like GPT provide a highly-advanced solution to the problems inherent in EL models, but simply naive prompts to LLMs do not work well. In the present work, we define ChatEL, which is a three-step framework to prompt LLMs to return accurate results. Overall the ChatEL framework improves the average F1 performance across 10 datasets by more than 2%. Finally, a thorough error analysis shows many instances with the ground truth labels were actually incorrect, and the labels predicted by ChatEL were actually correct. This indicates that the quantitative results presented in this paper may be a conservative estimate of the actual performance. All data and code are available as an open-source package on GitHub at https://github.com/yifding/In_Context_EL.

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ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models
Ning Bian | Xianpei Han | Le Sun | Hongyu Lin | Yaojie Lu | Ben He | Shanshan Jiang | Bin Dong

Large language models (LLMs) have made significant progress in NLP. However, their ability to memorize, represent, and leverage commonsense knowledge has been a well-known pain point. In this paper, we specifically focus on ChatGPT, a widely used and easily accessible LLM, and ask the following questions: (1) Can ChatGPT effectively answer commonsense questions? (2) Is ChatGPT aware of the underlying commonsense knowledge for answering a specific question? (3) Is ChatGPT knowledgeable in commonsense? (4) Can ChatGPT effectively leverage commonsense for answering questions? We conduct a series of experiments on 11 datasets to evaluate ChatGPT’s commonsense abilities, including answering commonsense questions, identifying necessary knowledge, generating knowledge descriptions, and using knowledge descriptions to answer questions again. Experimental results show that: (1) ChatGPT can achieve good QA accuracies in commonsense tasks, while still struggling with certain domains of datasets. (2) ChatGPT is knowledgeable, and can accurately generate most of the commonsense knowledge using knowledge prompts. (3) Despite its knowledge, ChatGPT is an inexperienced commonsense problem solver, which cannot precisely identify the needed commonsense for answering a specific question. These findings raise the need to explore improved mechanisms for effectively incorporating commonsense into LLMs like ChatGPT, such as better instruction following and commonsense guidance.

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ChatGPT Rates Natural Language Explanation Quality like Humans: But on Which Scales?
Fan Huang | Haewoon Kwak | Kunwoo Park | Jisun An

As AI becomes more integral in our lives, the need for transparency and responsibility grows. While natural language explanations (NLEs) are vital for clarifying the reasoning behind AI decisions, evaluating them through human judgments is complex and resource-intensive due to subjectivity and the need for fine-grained ratings. This study explores the alignment between ChatGPT and human assessments across multiple scales (i.e., binary, ternary, and 7-Likert scale). We sample 300 data instances from three NLE datasets and collect 900 human annotations for both informativeness and clarity scores as the text quality measurement. We further conduct paired comparison experiments under different ranges of subjectivity scores, where the baseline comes from 8,346 human annotations. Our results show that ChatGPT aligns better with humans in more coarse-grained scales. Also, paired comparisons and dynamic prompting (i.e., providing semantically similar examples in the prompt) improve the alignment. This research advances our understanding of large language models’ capabilities to assess the text explanation quality in different configurations for responsible AI development.

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ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness
Yufei Tao | Ameeta Agrawal | Judit Dombi | Tetyana Sydorenko | Jung In Lee

Recent advances in interactive large language models like ChatGPT have revolutionized various domains; however, their behavior in natural and role-play conversation settings remains underexplored. In our study, we address this gap by deeply investigating how ChatGPT behaves during conversations in different settings by analyzing its interactions in both a normal way and a role-play setting. We introduce a novel dataset of broad range of human-AI conversations annotated with user motives and model naturalness to examine (i) how humans engage with the conversational AI model, and (ii) how natural are AI model responses. Our study highlights the diversity of user motives when interacting with ChatGPT and variable AI naturalness, showing not only the nuanced dynamics of natural conversations between humans and AI, but also providing new avenues for improving the effectiveness of human-AI communication.

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ChatUIE: Exploring Chat-based Unified Information Extraction Using Large Language Models
Jun Xu | Mengshu Sun | Zhiqiang Zhang | Jun Zhou

Recent advancements in large language models have shown impressive performance in general chat. However, their domain-specific capabilities, particularly in information extraction, have certain limitations. Extracting structured information from natural language that deviates from known schemas or instructions has proven challenging for previous prompt-based methods. This motivated us to explore domain-specific modeling in chat-based language models as a solution for extracting structured information from natural language. In this paper, we present ChatUIE, an innovative unified information extraction framework built upon ChatGLM. Simultaneously, reinforcement learning is employed to improve and align various tasks that involve confusing and limited samples. Furthermore, we integrate generation constraints to address the issue of generating elements that are not present in the input. Our experimental results demonstrate that ChatUIE can significantly improve the performance of information extraction with a slight decrease in chatting ability.

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CHICA: A Developmental Corpus of Child-Caregiver’s Face-to-face vs. Video Call Conversations in Middle Childhood
Dhia Elhak Goumri | Abhishek Agrawal | Mitja Nikolaus | Hong Duc Thang Vu | Kübra Bodur | Elias Emmar | Cassandre Armand | Chiara Mazzocconi | Shreejata Gupta | Laurent Prévot | Benoit Favre | Leonor Becerra-Bonache | Abdellah Fourtassi

Existing studies of naturally occurring language-in-interaction have largely focused on the two ends of the developmental spectrum, i.e., early childhood and adulthood, leaving a gap in our knowledge about how development unfolds, especially across middle childhood. The current work contributes to filling this gap by introducing CHICA (for Child Interpersonal Communication Analysis), a developmental corpus of child-caregiver conversations at home, involving groups of French-speaking children aged 7, 9, and 11 years old. Each dyad was recorded twice: once in a face-to-face setting and once using computer-mediated video calls. For the face-to-face settings, we capitalized on recent advances in mobile, lightweight eye-tracking and head motion detection technology to optimize the naturalness of the recordings, allowing us to obtain both precise and ecologically valid data. Further, we mitigated the challenges of manual annotation by relying – to the extent possible – on automatic tools in speech processing and computer vision. Finally, to demonstrate the richness of this corpus for the study of child communicative development, we provide preliminary analyses comparing several measures of child-caregiver conversational dynamics across developmental age, modality, and communicative medium. We hope the current corpus will allow new discoveries into the properties and mechanisms of multimodal communicative development across middle childhood.

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Chinese Morpheme-informed Evaluation of Large Language Models
Yaqi Yin | Yue Wang | Yang Liu

Previous evaluations of large language models (LLMs) focused on the perspective of various tasks or abilities. In this paper, we propose to evaluate from a linguistic viewpoint and argue that morpheme, a potential linguistic feature that captures both word-formation and lexical semantics, is another suitable component for evaluation that remains largely unexplored. In light of this, we construct MorphEval, a morpheme-informed benchmark, including three datasets following the bottom-up levels of characters, words, and sentences in Chinese, and then evaluate representative LLMs with both zero- and few-shot settings under two metrics. From this perspective, we reveal three aspects of issues LLMs nowadays encounter: dysfunctions in morphology and syntax, challenges with the long-tailed distribution of semantics, and difficulties from cultural implications. In these scenarios, even a smaller Chinese-targeted model may outperform ChatGPT, highlighting the actual challenges LLMs face and the necessity of language-specific improvements when applied to non-English languages. This new approach could also help guide model enhancements as well as get extended to other languages.

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Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training
Longhui Zhang | Dingkun Long | Meishan Zhang | Yanzhao Zhang | Pengjun Xie | Min Zhang

Chinese sequence labeling tasks are sensitive to word boundaries. Although pretrained language models (PLM) have achieved considerable success in these tasks, current PLMs rarely consider boundary information explicitly. An exception to this is BABERT, which incorporates unsupervised statistical boundary information into Chinese BERT’s pre-training objectives. Building upon this approach, we input supervised high-quality boundary information to enhance BABERT’s learning, developing a semi-supervised boundary-aware PLM. To assess PLMs’ ability to encode boundaries, we introduce a novel “Boundary Information Metric” that is both simple and effective. This metric allows comparison of different PLMs without task-specific fine-tuning. Experimental results on Chinese sequence labeling datasets demonstrate that the improved BABERT version outperforms the vanilla version, not only in these tasks but also in broader Chinese natural language understanding tasks. Additionally, our proposed metric offers a convenient and accurate means of evaluating PLMs’ boundary awareness.

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CHisIEC: An Information Extraction Corpus for Ancient Chinese History
Xuemei Tang | Qi Su | Jun Wang | Zekun Deng

Natural Language Processing (NLP) plays a pivotal role in the realm of Digital Humanities (DH) and serves as the cornerstone for advancing the structural analysis of historical and cultural heritage texts. This is particularly true for the domains of named entity recognition (NER) and relation extraction (RE). In our commitment to expediting ancient history and culture, we present the “Chinese Historical Information Extraction Corpus”(CHisIEC). CHisIEC is a meticulously curated dataset designed to develop and evaluate NER and RE tasks, offering a resource to facilitate research in the field. Spanning a remarkable historical timeline encompassing data from 13 dynasties spanning over 1830 years, CHisIEC epitomizes the extensive temporal range and text heterogeneity inherent in Chinese historical documents. The dataset encompasses four distinct entity types and twelve relation types, resulting in a meticulously labeled dataset comprising 14,194 entities and 8,609 relations. To establish the robustness and versatility of our dataset, we have undertaken comprehensive experimentation involving models of various sizes and paradigms. Additionally, we have evaluated the capabilities of Large Language Models (LLMs) in the context of tasks related to ancient Chinese history. The dataset and code are available at https://github.com/tangxuemei1995/CHisIEC.

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Chitchat as Interference: Adding User Backstories to Task-Oriented Dialogues
Armand Stricker | Patrick Paroubek

During task-oriented dialogues (TODs), human users naturally introduce chitchat that is beyond the immediate scope of the task, interfering with the flow of the conversation. To address this issue without the need for expensive manual data creation, we use few-shot prompting with Llama-2-70B to enhance the MultiWOZ dataset with user backstories, a typical example of chitchat interference in TODs. We assess the impact of this addition by testing two models: one trained solely on TODs and another trained on TODs with a preliminary chitchat interaction. Our analysis demonstrates that our enhanced dataset poses a challenge for these systems. Moreover, we demonstrate that our dataset can be effectively used for training purposes, enabling a system to consistently acknowledge the user’s backstory while also successfully moving the task forward in the same turn, as confirmed by human evaluation. These findings highlight the benefits of generating novel chitchat-TOD scenarios to test TOD systems more thoroughly and improve their resilience to natural user interferences.

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Choice-75: A Dataset on Decision Branching in Script Learning
Zhaoyi Hou | Li Zhang | Chris Callison-Burch

Script learning studies how daily events unfold. It enables machines to reason about narratives with implicit information. Previous works mainly consider a script as a linear sequence of events while ignoring the potential branches that arise due to people’s circumstantial choices. We hence propose Choice-75, the first benchmark that challenges intelligent systems to make decisions given descriptive scenarios, containing 75 scripts and more than 600 scenarios. We also present preliminary results with current large language models (LLM). Although they demonstrate overall decent performances, there is still notable headroom in hard scenarios.

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C-Journal: A Journaling Application for Detecting and Classifying Cognitive Distortions Using Deep-Learning Based on a Crowd-sourced Dataset
Nada Elsharawi | Alia El Bolock

Cognitive distortions are negatively biased thinking patterns and erroneous self-statements resulting from and leading to logical errors in one’s own internal reasoning. Cognitive distortions have an adverse effect on mental health and can lead to mental health disorders in extreme cases. This paper belongs to a bigger project which aims to provide an application for detecting and classifying cognitive distortions in texts. As no public data sets were available for the task, the first contribution of the proposed work lies in providing an open-source labeled dataset of 14 cognitive distortions consisting of 34370 entries collected via crowd-sourcing, user questionnaires, and re-purposing emotions dataset from social media. The dataset is collected in cooperation with a licensed psychologist. We implemented a baseline model using Naïve Bayes and Count Vectorizer and different CNN, LSTM, and DNN classifiers to classify cognitive distortions based on the dataset. We investigated the usage of different word embeddings with the best-performing models. The best-performing model relied on a CNN with pre-trained Sentence-BERT embedding with an F1-score of 84 % for classifying cognitive distortions. The best-performing model was built into C-Journal, a free journaling and mood-tracking mobile application that pinpoints potential thinking distortions to the users.

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Claim-Centric and Sentiment Guided Graph Attention Network for Rumour Detection
Sajad Ramezani | Mauzama Firdaus | Lili Mou

Automatic rumour detection has gained attention due to the influence of social media on individuals and its pervasiveness. In this work, we construct a representation that takes into account the claim in the source tweet, considering both the propagation graph and the accompanying text alongside tweet sentiment. This is achieved through the implementation of a hierarchical attention mechanism, which not only captures the embedding of documents from individual word vectors but also combines these document representations as nodes within the propagation graph. Furthermore, to address potential overfitting concerns, we employ generative models to augment the existing datasets. This involves rephrasing the claims initially made in the source tweet, thereby creating a more diverse and robust dataset. In addition, we augment the dataset with sentiment labels to improve the performance of the rumour detection task. This holistic and refined approach yields a significant enhancement in the performance of our model across three distinct datasets designed for rumour detection. Quantitative and qualitative analysis proves the effectiveness of our methodology, surpassing the achievements of prior methodologies.

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Clarifying Completions: Evaluating How LLMs Respond to Incomplete Questions
Angus Addlesee | Oliver Lemon | Arash Eshghi

People understand and produce language incrementally on a word by word basis. This gives rise to many characteristic conversational phenomena including long mid-sentence pauses that are followed by incremental clarification requests (iCRs) intended to recover the rest of the truncated turn (see Fig. 1; (A), (B), (C)). The ability to generate iCRs is important in natural conversational AI systems, and crucial to their accessibility to users with memory impairment. In this paper, we collect, release and analyse SLUICE-CR: a large corpus of 3000 human produced iCRs. We then use this corpus to probe the incremental processing capability of a number of state of the art LLMs by evaluating the quality of the model’s generated iCRs in response to incomplete questions. Our evaluations show that the ability to generate contextually appropriate iCRs only emerges at larger LLM sizes, and only when prompted with example iCRs from our corpus. They also indicate that autoregressive LMs are, in principle, able to both understand and generate language incrementally.

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Classifying Social Media Users before and after Depression Diagnosis via Their Language Usage: A Dataset and Study
Falwah Alhamed | Julia Ive | Lucia Specia

Mental illness can significantly impact individuals’ quality of life. Analysing social media data to uncover potential mental health issues in individuals via their posts is a popular research direction. However, most studies focus on the classification of users suffering from depression versus healthy users, or on the detection of suicidal thoughts. In this paper, we instead aim to understand and model linguistic changes that occur when users transition from a healthy to an unhealthy state. Addressing this gap could lead to better approaches for earlier depression detection when signs are not as obvious as in cases of severe depression or suicidal ideation. In order to achieve this goal, we have collected the first dataset of textual posts by the same users before and after reportedly being diagnosed with depression. We then use this data to build multiple predictive models (based on SVM, Random Forests, BERT, RoBERTa, MentalBERT, GPT-3, GPT-3.5, Bard, and Alpaca) for the task of classifying user posts. Transformer-based models achieved the best performance, while large language models used off-the-shelf proved less effective as they produced random guesses (GPT and Bard) or hallucinations (Alpaca).

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Class-Incremental Few-Shot Event Detection
Kailin Zhao | Xiaolong Jin | Long Bai | Jiafeng Guo | Xueqi Cheng

Event detection is one of the fundamental tasks in information extraction and knowledge graph. However, a realistic event detection system often needs to deal with new event classes constantly. These new classes usually have only a few labeled instances as it is time-consuming and labor-intensive to annotate a large number of unlabeled instances. Therefore, this paper proposes a new task, called class-incremental few-shot event detection. Nevertheless, there are two problems (i.e., old knowledge forgetting and new class overfitting) in this task. To solve these problems, this paper further presents a novel knowledge distillation and prompt learning based method, called Prompt-KD. Specifically, to reduce the forgetting issue about old knowledge, Prompt-KD develops an attention based multi-teacher knowledge distillation framework, where the ancestor teacher model pre-trained on base classes is reused in all learning sessions, and the father teacher model derives the current student model via adaptation. On the other hand, in order to cope with the few-shot learning scenario and alleviate the corresponding new class overfitting problem, Prompt-KD is also equipped with a prompt learning mechanism. Extensive experiments on two benchmark datasets, i.e., FewEvent and MAVEN, demonstrate the state-of-the-art performance of Prompt-KD.

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CLASSLA-web: Comparable Web Corpora of South Slavic Languages Enriched with Linguistic and Genre Annotation
Nikola Ljubešić | Taja Kuzman

This paper presents a collection of highly comparable web corpora of Slovenian, Croatian, Bosnian, Montenegrin, Serbian, Macedonian, and Bulgarian, covering thereby the whole spectrum of official languages in the South Slavic language space. The collection of these corpora comprises a total of 13 billion tokens of texts from 26 million documents. The comparability of the corpora is ensured by a comparable crawling setup and the usage of identical crawling and post-processing technology. All the corpora were linguistically annotated with the state-of-the-art CLASSLA-Stanza linguistic processing pipeline, and enriched with document-level genre information via the Transformer-based multilingual X-GENRE classifier, which further enhances comparability at the level of linguistic annotation and metadata enrichment. The genre-focused analysis of the resulting corpora shows a rather consistent distribution of genres throughout the seven corpora, with variations in the most prominent genre categories being well-explained by the economic strength of each language community. A comparison of the distribution of genre categories across the corpora indicates that web corpora from less developed countries primarily consist of news articles. Conversely, web corpora from economically more developed countries exhibit a smaller proportion of news content, with a greater presence of promotional and opinionated texts.

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CLAUSE-ATLAS: A Corpus of Narrative Information to Scale up Computational Literary Analysis
Enrica Troiano | Piek T.J.M. Vossen

We introduce CLAUSE-ATLAS, a resource of XIX and XX century English novels annotated automatically. This corpus, which contains 41,715 labeled clauses, allows to study stories as sequences of eventive, subjective and contextual information. We use it to investigate if recent large language models, in particular gpt-3.5-turbo with 16k tokens of context, constitute promising tools to annotate large amounts of data for literary studies (we show that this is the case). Moreover, by analyzing the annotations so collected, we find that our clause-based approach to literature captures structural patterns within books, as well as qualitative differences between them.

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CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments
Savitha Sam Abraham | Marjan Alirezaie | Luc de Raedt

The integration of learning and reasoning is high on the research agenda in AI. Nevertheless, there is only a little attention to using existing background knowledge for reasoning about partially observed scenes to answer questions about the scene. Yet, we as humans use such knowledge frequently to infer plausible answers to visual questions (by eliminating all inconsistent ones). Such knowledge often comes in the form of constraints about objects and it tends to be highly domain or environment specific. We contribute a novel benchmark called CLEVR-POC for reasoning-intensive visual question answering (VQA) in partially observable environments under constraints. In CLEVR-POC, knowledge in the form of logical constraints needs to be leveraged in order to generate plausible answers to questions about a hidden object in a given partial scene. For instance, if one has the knowledge that all cups are colored either red, green or blue and that there is only one green cup, it becomes possible to deduce the color of an occluded cup as either red or blue, provided that all other cups, including the green one, are observed. Through experiments we observe that the performance of pre-trained vision language models like CLIP (approx. 22%) and a large language model (LLM) like GPT-4 (approx. 46%) on CLEVR-POC are not satisfactory, ascertaining the necessity for frameworks that can handle reasoning-intensive tasks where environment-specific background knowledge is available and crucial. Furthermore, our demonstration illustrates that a neuro-symbolic model, which integrates an LLM like GPT-4 with a visual perception network and a formal logical reasoner, exhibits exceptional performance on CLEVR-POC.

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CLFFRD: Curriculum Learning and Fine-grained Fusion for Multimodal Rumor Detection
Fan Xu | Lei Zeng | Bowei Zou | Ai Ti Aw | Huan Rong

In an era where rumors can propagate rapidly across social media platforms such as Twitter and Weibo, automatic rumor detection has garnered considerable attention from both academia and industry. Existing multimodal rumor detection models often overlook the intricacies of sample difficulty, e.g., text-level difficulty, image-level difficulty, and multimodal-level difficulty, as well as their order when training. Inspired by the concept of curriculum learning, we propose the Curriculum Learning and Fine-grained Fusion-driven multimodal Rumor Detection (CLFFRD) framework, which employs curriculum learning to automatically select and train samples according to their difficulty at different training stages. Furthermore, we introduce a fine-grained fusion strategy that unifies entities from text and objects from images, enhancing their semantic cohesion. We also propose a novel data augmentation method that utilizes linear interpolation between textual and visual modalities to generate diverse data. Additionally, our approach incorporates deep fusion for both intra-modality (e.g., text entities and image objects) and inter-modality (e.g., CLIP and social graph) features. Extensive experimental results demonstrate that CLFFRD outperforms state-of-the-art models on both English and Chinese benchmark datasets for rumor detection in social media.

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CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment
Feiteng Fang | Liang Zhu | Xi Feng | Jinchang Hou | Qixuan Zhao | Chengming Li | Xiping Hu | Ruifeng Xu | Min Yang

Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial and comprehensible ways to users. However, a longstanding challenge in human alignment techniques based on reinforcement learning lies in their inherent complexity and difficulty in training. To address this challenge, we present a simple yet effective Contrastive Learning Framework for Human Alignment (CLHA) to align LLMs with human preferences directly. CLHA employs a novel rescoring strategy to evaluate the noise within the data by considering its inherent quality and dynamically adjusting the training process. Simultaneously, CLHA utilizes pairwise contrastive loss and adaptive supervised fine-tuning loss to adaptively modify the likelihood of generating responses, ensuring enhanced alignment with human preferences. Using advanced methods, CLHA surpasses other algorithms, showcasing superior performance in terms of reward model scores, automatic evaluations, and human assessments on the widely used “Helpful and Harmless” dataset.

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CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean
Eunsu Kim | Juyoung Suk | Philhoon Oh | Haneul Yoo | James Thorne | Alice Oh

Despite the rapid development of large language models (LLMs) for the Korean language, there remains an obvious lack of benchmark datasets that test the requisite Korean cultural and linguistic knowledge. Because many existing Korean benchmark datasets are derived from the English counterparts through translation, they often overlook the different cultural contexts. For the few benchmark datasets that are sourced from Korean data capturing cultural knowledge, only narrow tasks such as hate speech detection are offered. To address this gap, we introduce a benchmark of Cultural and Linguistic Intelligence in Korean (CLIcK), a dataset comprising 1,995 QA pairs. CLIcK sources its data from official Korean exams and textbooks, partitioning the questions into eleven categories under the two main categories of language and culture. For each instance in click, we provide fine-grained annotation of which cultural and linguistic knowledge is required to correctly answer the question. Using CLIcK, we test 13 language models to assess their performance. Our evaluation uncovers insights into their performances across the categories, as well as the diverse factors affecting their comprehension. CLIcK offers the first large-scale comprehensive Korean-centric analysis of LLMs’ proficiency in Korean language and culture.

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Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles
Andrea Zugarini | Kamyar Zeinalipour | Surya Sai Kadali | Marco Maggini | Marco Gori | Leonardo Rigutini

Crossword puzzles are popular linguistic games often used as tools to engage students in learning. Educational crosswords are characterized by less cryptic and more factual clues that distinguish them from traditional crossword puzzles. Despite there exist several publicly available clue-answer pair databases for traditional crosswords, educational clue-answer pairs datasets are missing. In this article, we propose a methodology to build educational clue generation datasets that can be used to instruct Large Language Models (LLMs). By gathering from Wikipedia pages informative content associated with relevant keywords, we use Large Language Models to automatically generate pedagogical clues related to the given input keyword and its context. With such an approach, we created clue-instruct, a dataset containing 44,075 unique examples with text-keyword pairs associated with three distinct crossword clues. We used clue-instruct to instruct different LLMs to generate educational clues from a given input content and keyword. Both human and automatic evaluations confirmed the quality of the generated clues, thus validating the effectiveness of our approach.

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CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation
Yujie Shao | Xinrong Yao | Xingwei Qu | Chenghua Lin | Shi Wang | Wenhao Huang | Ge Zhang | Jie Fu

Metaphor is a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. This paper introduces a large-scale high quality annotated Chinese Metaphor Corpus, which comprises around 28K sentences drawn from a diverse range of Chinese literary sources, such as poems, prose, song lyrics, etc. To ensure the accuracy and consistency of our annotations, we introduce a comprehensive set of guidelines. These guidelines address the facets of metaphor annotation, including identifying tenors, vehicles, and grounds to handling the complexities of similes, personifications, juxtapositions, and hyperboles. Breaking tradition, our approach to metaphor generation emphasizes tenors and their distinct features rather than the conventional combination of tenors and vehicles. By integrating “ground” as a CoT (Chain of Thoughts) input, we are able to generate metaphors that resonate more with real-world intuition. We test generative models such as Belle, Baichuan, and Chinese-alpaca-33B using our annotated corpus. These models are able to generate creative and fluent metaphor sentences more frequently induced by selected samples from our dataset, demonstrating the value of our corpus for Chinese metaphor research.

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CMNEE:A Large-Scale Document-Level Event Extraction Dataset Based on Open-Source Chinese Military News
Mengna Zhu | Zijie Xu | Kaisheng Zeng | Kaiming Xiao | Mao Wang | Wenjun Ke | Hongbin Huang

Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis and decision assistance. However, event extraction in the military field faces the data scarcity problem, which impedes the research of event extraction models in this domain. To alleviate this problem, we propose CMNEE, a large-scale, document-level open-source Chinese Military News Event Extraction dataset. It contains 17,000 documents and 29,223 events, which are all manually annotated based on a pre-defined schema for the military domain including 8 event types and 11 argument role types. We designed a two-stage, multi-turns annotation strategy to ensure the quality of CMNEE and reproduced several state-of-the-art event extraction models with a systematic evaluation. The experimental results on CMNEE fall shorter than those on other domain datasets obviously, which demonstrates that event extraction for military domain poses unique challenges and requires further research efforts. Our code and data can be obtained from https://github.com/Mzzzhu/CMNEE. Keywords: Corpus,Information Extraction, Information Retrieval, Knowledge Discovery/Representation

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CM-Off-Meme: Code-Mixed Hindi-English Offensive Meme Detection with Multi-Task Learning by Leveraging Contextual Knowledge
Gitanjali Kumari | Dibyanayan Bandyopadhyay | Asif Ekbal | Vinutha B. NarayanaMurthy

Detecting offensive content in internet memes is challenging as it needs additional contextual knowledge. While previous works have only focused on detecting offensive memes, classifying them further into implicit and explicit categories depending on their severity is still a challenging and underexplored area. In this work, we present an end-to-end multitask model for addressing this challenge by empirically investigating two correlated tasks simultaneously: (i) offensive meme detection and (ii) explicit-implicit offensive meme detection by leveraging the two self-supervised pre-trained models. The first pre-trained model, referred to as the “knowledge encoder,” incorporates contextual knowledge of the meme. On the other hand, the second model, referred to as the “fine-grained information encoder”, is trained to understand the obscure psycho-linguistic information of the meme. Our proposed model utilizes contrastive learning to integrate these two pre-trained models, resulting in a more comprehensive understanding of the meme and its potential for offensiveness. To support our approach, we create a large-scale dataset, CM-Off-Meme, as there is no publicly available such dataset for the code-mixed Hindi-English (Hinglish) domain. Empirical evaluation, including both qualitative and quantitative analysis, on the CM-Off-Meme dataset demonstrates the effectiveness of the proposed model in terms of cross-domain generalization.

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CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite
Yifei Yuan | Chen Shi | Wang Runze | Liyi Chen | Renjun Hu | Zengming Zhang | Feijun Jiang | Wai Lam

Generative query rewrite generates reconstructed query rewrites using the conversation history while rely heavily on gold rewrite pairs that are expensive to obtain. Recently, few-shot learning is gaining increasing popularity for this task, whereas these methods are sensitive to the inherent noise due to limited data size. Besides, both attempts face performance degradation when there exists language style shift between training and testing cases. To this end, we study low-resource generative conversational query rewrite that is robust to both noise and language style shift. The core idea is to utilize massive unlabeled data to make further improvements via a contrastive co-training paradigm. Specifically, we co-train two dual models (namely Rewriter and Simplifier) such that each of them provides extra guidance through pseudo-labeling for enhancing the other in an iterative manner. We also leverage contrastive learning with data augmentation, which enables our model pay more attention on the truly valuable information than the noise. Extensive experiments demonstrate the superiority of our model under both few-shot and zero-shot scenarios. We also verify the better generalization ability of our model when encountering language style shift.

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CoANZSE Audio: Creation of an Online Corpus for Linguistic and Phonetic Analysis of Australian and New Zealand Englishes
Steven Coats

CoANZSE Audio is a searchable online version of the Corpus of Australian and New Zealand Spoken English, a 195-million-word collection of geo-located YouTube transcripts of local government channels. In addition to the part-of-speech-tagged and lemmatized transcript data, CoANZSE Audio provides access to almost all of the underlying audio, as well as to forced alignments of the audio with transcript content, in Praat’s TextGrid format. This paper describes the methods used to create the corpus from open-source tools and the architecture of the CoANZSE Audio website. Two possible linguistic analyses based on CoANZSE Audio data are described: use of double modals, a rare syntactic feature, and raising of the mid front vowel /ɛ/ in New Zealand English. CoANZSE Audio can be considered to be among the first large, free, fully searchable online corpora containing data suitable for acoustic phonetic analyses in addition to lexical, grammatical, and discourse properties of Australian and New Zealand Englishes.

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Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models
Atsushi Keyaki | Ribeka Keyaki

Fine-tuning in information retrieval systems using pre-trained language models (PLM-based IR) requires learning query representations and query-document relations, in addition to downstream task-specific learning. This study introduces coarse-tuning as an intermediate learning stage that bridges pre-training and fine-tuning. By learning query representations and query-document relations in coarse-tuning, we aim to reduce the load of fine-tuning and improve the learning effect of downstream IR tasks. We propose Query-Document Pair Prediction (QDPP) for coarse-tuning, which predicts the appropriateness of query-document pairs. Evaluation experiments show that the proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc document retrieval datasets. Furthermore, the results of the query prediction task suggested that coarse-tuning facilitated learning of query representation and query-document relations.

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CoBaLD Annotation: The Enrichment of the Enhanced Universal Dependencies with the Semantical Pattern
Maria Andreevna Petrova | Alexandra M. Ivoylova | Anastasia Tishchenkova

The paper is devoted to the annotation format aimed at morphological, syntactic and especially semantic markup. The format combines the Enhanced UD morphosyntax and the Compreno semantic pattern, enriching the UD annotation with word meanings and labels for semantic relations between words. To adapt the Compreno semantics for the current purpose, we reduced the number of the semantic fields denoting lexical meanings by using hyperonym fields. Moreover, we used a generalized variant of the semantic relations as the original roles possess rather narrow meanings which makes them too numerous. Creating such a format demands the Compreno-to-UD morphosyntax conversion as well, which, in turn, demands solving the asymmetry problem between the models. The asymmetry concerns tokenization, lemmatization, POS-tagging, sets of grammatical features and dependency heads. To overcome this problem, the Compreno-to-UD converter was created. As an application, the work presents a 150,000 token corpus of English news annotated according to the standard.

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CoCoMIC: Code Completion by Jointly Modeling In-file and Cross-file Context
Yangruibo Ding | Zijian Wang | Wasi U. Ahmad | Murali Krishna Ramanathan | Ramesh Nallapati | Parminder Bhatia | Dan Roth | Bing Xiang

While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i.e., in-file context, but ignore the rich semantics in other files within the same project, i.e., project-level cross-file context, a critical source of information that is especially useful in modern modular software development. Such overlooking constrains code LMs’ capacity in code completion, leading to unexpected behaviors such as generating hallucinated class member functions or function calls with unexpected arguments. In this work, we propose CoCoMIC, a novel framework that jointly learns the in-file and cross-file context on top of code LMs. To empower CoCoMIC, we develop CCFinder, a static-analysis-based tool that locates and retrieves the most relevant project-level cross-file context for code completion. CoCoMIC successfully improves the existing code LM with a 33.94% relative increase in exact match and 28.69% in identifier matching for code completion when the cross-file context is provided. Finally, we perform a series of ablation studies and share valuable insights for future research on integrating cross-file context into code LMs.

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Code Defect Detection Using Pre-trained Language Models with Encoder-Decoder via Line-Level Defect Localization
Jimin An | YunSeok Choi | Jee-Hyong Lee

Recently, code Pre-trained Language Models (PLMs) trained on large amounts of code and comment, have shown great success in code defect detection tasks. However, most PLMs simply treated the code as a single sequence and only used the encoder of PLMs to determine if there exist defects in the entire code. For a more analyzable and explainable approach, it is crucial to identify which lines contain defects. In this paper, we propose a novel method for code defect detection that integrates line-level defect localization into a unified training process. To identify code defects at the line-level, we convert the code into a sequence separated by lines using a special token. Then, to utilize the characteristic that both the encoder and decoder of PLMs process information differently, we leverage both the encoder and decoder for line-level defect localization. By learning code defect detection and line-level defect localization tasks in a unified manner, our proposed method promotes knowledge sharing between the two tasks. We demonstrate that our proposed method significantly improves performance on four benchmark datasets for code defect detection. Additionally, we show that our method can be easily integrated with ChatGPT.

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Code-Mixed Probes Show How Pre-Trained Models Generalise on Code-Switched Text
Frances Adriana Laureano De Leon | Harish Tayyar Madabushi | Mark Lee

Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages. Despite its widespread use online and recent research trends in this area, research in code-switching presents unique challenges, primarily stemming from the scarcity of labelled data and available resources. In this study we investigate how pre-trained Language Models handle code-switched text in three dimensions: a) the ability of PLMs to detect code-switched text, b) variations in the structural information that PLMs utilise to capture code-switched text, and c) the consistency of semantic information representation in code-switched text. To conduct a systematic and controlled evaluation of the language models in question, we create a novel dataset of well-formed naturalistic code-switched text along with parallel translations into the source languages. Our findings reveal that pre-trained language models are effective in generalising to code-switched text, shedding light on abilities of these models to generalise representations to CS corpora. We release all our code and data, including the novel corpus, at https://github.com/francesita/code-mixed-probes.

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Code-Mixed Text Augmentation for Latvian ASR
Martins Kronis | Askars Salimbajevs | Mārcis Pinnis

Code-mixing has become mainstream in the modern, globalised world and affects low-resource languages, such as Latvian, in particular. Solutions to developing an automatic speech recognition system (ASR) for code-mixed speech often rely on specially created audio-text corpora, which are expensive and time-consuming to create. In this work, we attempt to tackle code-mixed Latvian-English speech recognition by improving the language model (LM) of a hybrid ASR system. We make a distinction between inflected transliterations and phonetic transcriptions as two different foreign word types. We propose an inflected transliteration model and a phonetic transcription model for the automatic generation of said word types. We then leverage a large human-translated English-Latvian parallel text corpus to generate synthetic code-mixed Latvian sentences by substituting in generated foreign words. Using the newly created augmented corpora, we train a new LM and combine it with our existing Latvian acoustic model (AM). For evaluation, we create a specialised foreign word test set on which our methods yield up to 15% relative CER improvement. We then further validate these results in a human evaluation campaign.

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Cognitive Information Bottleneck: Extracting Minimal Sufficient Cognitive Language Processing Signals
Yuto Harada | Yohei Oseki

In Reinforcement Learning from Human Feedback (RLHF), explicit human feedback, such as rankings, is employed to align Natural Language Processing (NLP) models with human preferences. In contrast, the potential of implicit human feedback, encompassing cognitive processing signals like eye-tracking and brain activity, remains underexplored. These signals capture unconscious human responses but are often marred by noise and redundancy, complicating their application to specific tasks. To address this issue, we introduce the Cognitive Information Bottleneck (CIB), a method that extracts only the task-relevant information from cognitive processing signals. Grounded in the principles of the information bottleneck, CIB aims to learn representations that maximize the mutual information between the representations and targets while minimizing the mutual information between inputs and representations. By employing CIB to filter out redundant information from cognitive processing signals, our goal is to provide representations that are both minimal and sufficient. This approach enables more efficient fitting of models to inputs. Our results show that the proposed method outperforms existing methods in efficiently compressing various cognitive processing signals and significantly enhances performance on downstream tasks. Evaluated on public datasets, our model surpasses contemporary state-of-the-art models. Furthermore, by analyzing these compressed representations, we offer insights into how cognitive processing signals can be leveraged to improve performance.

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CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction
Xiang Wei | Yufeng Chen | Ning Cheng | Xingyu Cui | Jinan Xu | Wenjuan Han

In order to construct or extend entity-centric and event-centric knowledge graphs (KG and EKG), the information extraction (IE) annotation toolkit is essential. However, existing IE toolkits have several non-trivial problems, such as not supporting multi-tasks, and not supporting automatic updates. In this work, we present CollabKG, a learnable human-machine-cooperative IE toolkit for KG and EKG construction. Specifically, for the multi-task issue, CollabKG unifies different IE subtasks, including named entity recognition (NER), entity-relation triple extraction (RE), and event extraction (EE), and supports both KG and EKG. Then, combining advanced prompting-based IE technology, the human-machine-cooperation mechanism with Large Language Models (LLMs) as the assistant machine is presented which can provide a lower cost as well as a higher performance. Lastly, owing to the two-way interaction between the human and machine, CollabKG with learning ability allows self-renewal. Besides, CollabKG has several appealing features (e.g., customization, training-free, and label propagation) that make the system powerful and high-productivity. We holistically compare our toolkit with other existing tools on these features. Human evaluation quantitatively illustrates that CollabKG significantly improves annotation quality, efficiency, and stability simultaneously.

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Collecting and Analyzing Dialogues in a Tagline Co-Writing Task
Xulin Zhou | Takuma Ichikawa | Ryuichiro Higashinaka

The potential usage scenarios of dialogue systems will be greatly expanded if they are able to collaborate more creatively with humans. Many studies have examined ways of building such systems, but most of them focus on problem-solving dialogues, and relatively little research has been done on systems that can engage in creative collaboration with users. In this study, we designed a tagline co-writing task in which two people collaborate to create taglines via text chat, created an interface for data collection, and collected dialogue logs, editing logs, and questionnaire results. In total, we collected 782 Japanese dialogues. We describe the characteristic interactions comprising the tagline co-writing task and report the results of our analysis, in which we examined the kind of utterances that appear in the dialogues as well as the most frequent expressions found in highly rated dialogues in subjective evaluations. We also analyzed the relationship between subjective evaluations and workflow utilized in the dialogues and the interplay between taglines and utterances.

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Collecting Human-Agent Dialogue Dataset with Frontal Brain Signal toward Capturing Unexpressed Sentiment
Shun Katada | Ryu Takeda | Kazunori Komatani

Multimodal information such as text and audiovisual data has been used for emotion/sentiment estimation during human-agent dialogue; however, user sentiments are not necessarily expressed explicitly during dialogues. Biosignals such as brain signals recorded using an electroencephalogram (EEG) sensor have been the subject of focus in affective computing regions to capture unexpressed emotional changes in a controlled experimental environment. In this study, we collect and analyze multimodal data with an EEG during a human-agent dialogue toward capturing unexpressed sentiment. Our contributions are as follows: (1) a new multimodal human-agent dialogue dataset is created, which includes not only text and audiovisual data but also frontal EEGs and physiological signals during the dialogue. In total, about 500-minute chat dialogues were collected from thirty participants aged 20 to 70. (2) We present a novel method for dealing with eye-blink noise for frontal EEGs denoising. This method applies facial landmark tracking to detect and delete eye-blink noise. (3) An experimental evaluation showed the effectiveness of the frontal EEGs. It improved sentiment estimation performance when used with other modalities by multimodal fusion, although it only has three channels.

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Collecting Linguistic Resources for Assessing Children’s Pronunciation of Nordic Languages
Anne Marte Haug Olstad | Anna Smolander | Sofia Strömbergsson | Sari Ylinen | Minna Lehtonen | Mikko Kurimo | Yaroslav Getman | Tamás Grósz | Xinwei Cao | Torbjørn Svendsen | Giampiero Salvi

This paper reports on the experience collecting a number of corpora of Nordic languages spoken by children. The aim of the data collection is providing annotated data to develop and evaluate computer assisted pronunciation assessment systems both for non-native children learning a Nordic language (L2) and for L1 children with speech sound disorder (SSD). The paper presents the challenges encountered recording and annotating data for Finnish, Swedish and Norwegian, as well as the ethical considerations related with making this data publicly available. We hope that sharing this experience will encourage others to collect similar data for other languages. Of the different data collections, we were able to make the Norwegian corpus publicly available in the hope that it will serve as a reference in pronunciation assessment research.

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Combining Discourse Coherence with Large Language Models for More Inclusive, Equitable, and Robust Task-Oriented Dialogue
Katherine Atwell | Mert Inan | Anthony B. Sicilia | Malihe Alikhani

Large language models (LLMs) are capable of generating well-formed responses, but using LLMs to generate responses on the fly is not yet feasible for many task-oriented systems. Modular architectures are often still required for safety and privacy guarantees on the output. We hypothesize that an offline generation approach using discourse theories, formal grammar rules, and LLMs can allow us to generate human-like, coherent text in a more efficient, robust, and inclusive manner within a task-oriented setting. To this end, we present the first discourse-aware multimodal task-oriented dialogue system that combines discourse theories with offline LLM generation. We deploy our bot as an app to the general public and keep track of the user ratings for six months. Our user ratings show an improvement from 2.8 to 3.5 out of 5 with the introduction of discourse coherence theories. We also show that our model reduces misunderstandings in the dialect of African-American Vernacular English from 93% to 57%. While terms of use prevent us from releasing our entire codebase, we release our code in a format that can be integrated into most existing dialogue systems.

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COMET for Low-Resource Machine Translation Evaluation: A Case Study of English-Maltese and Spanish-Basque
Júlia Falcão | Claudia Borg | Nora Aranberri | Kurt Abela

Trainable metrics for machine translation evaluation have been scoring the highest correlations with human judgements in the latest meta-evaluations, outperforming traditional lexical overlap metrics such as BLEU, which is still widely used despite its well-known shortcomings. In this work we look at COMET, a prominent neural evaluation system proposed in 2020, to analyze the extent of its language support restrictions, and to investigate strategies to extend this support to new, under-resourced languages. Our case study focuses on English-Maltese and Spanish-Basque. We run a crowd-based evaluation campaign to collect direct assessments and use the annotated dataset to evaluate COMET-22, further fine-tune it, and to train COMET models from scratch for the two language pairs. Our analysis suggests that COMET’s performance can be improved with fine-tuning, and that COMET can be highly susceptible to the distribution of scores in the training data, which especially impacts low-resource scenarios.

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COMICORDA: Dialogue Act Recognition in Comic Books
Jiri Martinek | Pavel Kral | Ladislav Lenc | Josef Baloun

Dialogue act (DA) recognition is usually realized from a speech signal that is transcribed and segmented into text. However, only a little work in DA recognition from images exists. Therefore, this paper concentrates on this modality and presents a novel DA recognition approach for image documents, namely comic books. To the best of our knowledge, this is the first study investigating dialogue acts from comic books and represents the first steps to building a model for comic book understanding. The proposed method is composed of the following steps: speech balloon segmentation, optical character recognition (OCR), and DA recognition itself. We use YOLOv8 for balloon segmentation, Google Vision for OCR, and Transformer-based models for DA classification. The experiments are performed on a newly created dataset comprising 1,438 annotated comic panels. It contains bounding boxes, transcriptions, and dialogue act annotation. We have achieved nearly 98% average precision for speech balloon segmentation and exceeded the accuracy of 70% for the DA recognition task. We also present an analysis of dialogue structure in the comics domain and compare it with the standard DA datasets, representing another contribution of this paper.

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Common European Language Data Space
Georg Rehm | Stelios Piperidis | Khalid Choukri | Andrejs Vasiļjevs | Katrin Marheinecke | Victoria Arranz | Aivars Bērziņš | Miltos Deligiannis | Dimitris Galanis | Maria Giagkou | Katerina Gkirtzou | Dimitris Gkoumas | Annika Grützner-Zahn | Athanasia Kolovou | Penny Labropoulou | Andis Lagzdiņš | Elena Leitner | Valérie Mapelli | Hélène Mazo | Simon Ostermann | Stefania Racioppa | Mickaël Rigault | Leon Voukoutis

The Common European Language Data Space (LDS) is an integral part of the EU data strategy, which aims at developing a single market for data. Its decentralised technical infrastructure and governance scheme are currently being developed by the LDS project, which also has dedicated tasks for proof-of-concept prototypes, handling legal aspects, raising awareness and promoting the LDS through events and social media channels. The LDS is part of a broader vision for establishing all necessary components to develop European large language models.

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Common Ground Tracking in Multimodal Dialogue
Ibrahim Khalil Khebour | Kenneth Lai | Mariah Bradford | Yifan Zhu | Richard A. Brutti | Christopher Tam | Jingxuan Tu | Benjamin A. Ibarra | Nathaniel Blanchard | Nikhil Krishnaswamy | James Pustejovsky

Within Dialogue Modeling research in AI and NLP, considerable attention has been spent on “dialogue state tracking” (DST), which is the ability to update the representations of the speaker’s needs at each turn in the dialogue by taking into account the past dialogue moves and history. Less studied but just as important to dialogue modeling, however, is “common ground tracking” (CGT), which identifies the shared belief space held by all of the participants in a task-oriented dialogue: the task-relevant propositions all participants accept as true. In this paper we present a method for automatically identifying the current set of shared beliefs and ”questions under discussion” (QUDs) of a group with a shared goal. We annotate a dataset of multimodal interactions in a shared physical space with speech transcriptions, prosodic features, gestures, actions, and facets of collaboration, and operationalize these features for use in a deep neural model to predict moves toward construction of common ground. Model outputs cascade into a set of formal closure rules derived from situated evidence and belief axioms and update operations. We empirically assess the contribution of each feature type toward successful construction of common ground relative to ground truth, establishing a benchmark in this novel, challenging task.

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Comparative Analysis of Sign Language Interpreting Agents Perception: A Study of the Deaf
Alfarabi Imashev | Nurziya Oralbayeva | Gulmira Baizhanova | Anara Sandygulova

Prior research on sign language recognition has already demonstrated encouraging outcomes in achieving highly accurate and dependable automatic sign language recognition. The use of virtual characters as virtual assistants has significantly increased in the past decade. However, the progress in sign language generation and output that closely resembles physiologically believable human motions is still in its early stages. This assertion explains the lack of progress in virtual intelligent signing generative systems. Aside from the development of signing systems, scholarly research have revealed a significant deficiency in evaluating sign language generation systems by those who are deaf and use sign language. This paper presents the findings of a user study conducted with deaf signers. The study is aimed at comparing a state-of-the-art sign language generation system with a skilled sign language interpreter. The study focused on testing established metrics to gain insights into usability of such metrics for deaf signers and how deaf signers perceive signing agents.

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Comparing Static and Contextual Distributional Semantic Models on Intrinsic Tasks: An Evaluation on Mandarin Chinese Datasets
A Pranav | Yan Cong | Emmanuele Chersoni | Yu-Yin Hsu | Alessandro Lenci

The field of Distributional Semantics has recently undergone important changes, with the contextual representations produced by Transformers taking the place of static word embeddings models. Noticeably, previous studies comparing the two types of vectors have only focused on the English language and a limited number of models. In our study, we present a comparative evaluation of static and contextualized distributional models for Mandarin Chinese, focusing on a range of intrinsic tasks. Our results reveal that static models remain stronger for some of the classical tasks that consider word meaning independent of context, while contextualized models excel in identifying semantic relations between word pairs and in the categorization of words into abstract semantic classes.

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Comparison of Conventional Hybrid and CTC/Attention Decoders for Continuous Visual Speech Recognition
David Gimeno-Gómez | Carlos-D. Martínez-Hinarejos

Thanks to the rise of deep learning and the availability of large-scale audio-visual databases, recent advances have been achieved in Visual Speech Recognition (VSR). Similar to other speech processing tasks, these end-to-end VSR systems are usually based on encoder-decoder architectures. While encoders are somewhat general, multiple decoding approaches have been explored, such as the conventional hybrid model based on Deep Neural Networks combined with Hidden Markov Models (DNN-HMM) or the Connectionist Temporal Classification (CTC) paradigm. However, there are languages and tasks in which data is scarce, and in this situation, there is not a clear comparison between different types of decoders. Therefore, we focused our study on how the conventional DNN-HMM decoder and its state-of-the-art CTC/Attention counterpart behave depending on the amount of data used for their estimation. We also analyzed to what extent our visual speech features were able to adapt to scenarios for which they were not explicitly trained, either considering a similar dataset or another collected for a different language. Results showed that the conventional paradigm reached recognition rates that improve the CTC/Attention model in data-scarcity scenarios along with a reduced training time and fewer parameters.

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Comparison of the Intimacy Process between Real and Acting-based Long-term Text Chats
Tsunehiro Arimoto | Hiroaki Sugiyama | Hiromi Narimatsu | Masahiro Mizukami

Long-term chatbots are expected to develop relationships with users. The major trend in this field’s recent long-term chatbot studies is to train systems with virtual long-term chat data called Multi-Session Chat (MSC), which collects text chat from multiple sessions of crowd workers playing the roles of speakers with defined personas. However, no investigation has attempted to determine whether such virtual long-term chat can successfully simulate relationship-building between speakers. To clarify the difference between an actual long-term intimacy process and an MSC intimacy process, this study collects real long-term chat and MSC in Japanese and compares them in terms of speech form and dialogue acts. The results of analyzing these factors suggest that MSC have an unnatural tendency to behave as if they have a close relationship with non-polite speech levels compared to actual long-term chats, but also as if they have a shallow relationship with more questions than real long-term chats.

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Complex Word Identification: A Comparative Study between ChatGPT and a Dedicated Model for This Task
Abdelhak Kelious | Mathieu Constant | Christophe Coeur

There are several works in natural language processing for identifying lexical complexity. This can be for various reasons, either for simplification, the selection of more suitable content, or for other specific tasks. Words can have multiple definitions and degrees of complexity depending on the context in which they appear. One solution being investigated is lexical complexity prediction, where computational methods are used to evaluate the difficulty of vocabulary for language learners and offer personalized assistance. In this work, we explore deep learning methods to assess the complexity of a word based on its context. Specifically, we investigate how to use pre-trained language models to encode both the sentence and the target word, and then fine-tune them by combining them with additional frequency-based features. Our approach achieved superior results compared to the best systems in SemEval-2021 (Shardlow et al., 2021), as demonstrated by an R2 score of 0.65. Finally, we carry out a comparative study with ChatGPT to assess its potential for predicting lexical complexity, to see whether prompt engineering can be an alternative to this task, we will discuss the advantages and limitations of ChatGPT.

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Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Ahmad Idrissi-Yaghir | Amin Dada | Henning Schäfer | Kamyar Arzideh | Giulia Baldini | Jan Trienes | Max Hasin | Jeanette Bewersdorff | Cynthia S. Schmidt | Marie Bauer | Kaleb E. Smith | Jiang Bian | Yonghui Wu | Jörg Schlötterer | Torsten Zesch | Peter A. Horn | Christin Seifert | Felix Nensa | Jens Kleesiek | Christoph M. Friedrich

Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can struggle in specialized domains such as medicine, where unique domain-specific terminologies, domain-specific abbreviations, and varying document structures are common. This paper explores strategies for adapting these models to domain-specific requirements, primarily through continuous pre-training on domain-specific data. We pre-trained several German medical language models on 2.4B tokens derived from translated public English medical data and 3B tokens of German clinical data. The resulting models were evaluated on various German downstream tasks, including named entity recognition (NER), multi-label classification, and extractive question answering. Our results suggest that models augmented by clinical and translation-based pre-training typically outperform general domain models in medical contexts. We conclude that continuous pre-training has demonstrated the ability to match or even exceed the performance of clinical models trained from scratch. Furthermore, pre-training on clinical data or leveraging translated texts have proven to be reliable methods for domain adaptation in medical NLP tasks.

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Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases
Yuqi Liu | Guanyi Chen | Kees van Deemter

Theoretical linguists have suggested that some languages (e.g., Chinese and Japanese) are “cooler” than other languages based on the observation that the intended meaning of phrases in these languages depends more on their contexts. As a result, many expressions in these languages are shortened, and their meaning is inferred from the context. In this paper, we focus on the omission of the plurality and definiteness markers in Chinese noun phrases (NPs) to investigate the predictability of their intended meaning given the contexts. To this end, we built a corpus of Chinese NPs, each of which is accompanied by its corresponding context, and by labels indicating its singularity/plurality and definiteness/indefiniteness. We carried out corpus assessments and analyses. The results suggest that Chinese speakers indeed drop plurality and definiteness markers very frequently. Building on the corpus, we train a bank of computational models using both classic machine learning models and state-of-the-art pre-trained language models to predict the plurality and definiteness of each NP. We report on the performance of these models and analyse their behaviours.

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CONAN-MT-SP: A Spanish Corpus for Counternarrative Using GPT Models
María Estrella Vallecillo Rodríguez | Maria Victoria Cantero Romero | Isabel Cabrera De Castro | Arturo Montejo Ráez | María Teresa Martín Valdivia

This paper describes the automated generation of CounterNarratives (CNs) for Hate Speech (HS) in Spanish using GPT-based models. Our primary objective is to evaluate the performance of these models in comparison to human capabilities. For this purpose, the English CONAN Multitarget corpus is taken as a starting point and we use the DeepL API to automatically translate into Spanish. Two GPT-based models, GPT-3 and GPT-4, are applied to the HS segment through a few-shot prompting strategy to generate a new CN. As a consequence of our research, we have created a high quality corpus in Spanish that includes the original HS-CN pairs translated into Spanish, in addition to the CNs generated automatically with the GPT models and that have been evaluated manually. The resulting CONAN-MT-SP corpus and its evaluation will be made available to the research community, representing the most extensive linguistic resource of CNs in Spanish to date. The results demonstrate that, although the effectiveness of GPT-4 outperforms GPT-3, both models can be used as systems to automatically generate CNs to combat the HS. Moreover, these models consistently outperform human performance in most instances.

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Conceptual Pacts for Reference Resolution Using Small, Dynamically Constructed Language Models: A Study in Puzzle Building Dialogues
Julian Hough | Sina Zarrieß | Casey Kennington | David Schlangen | Massimo Poesio

Using Brennan and Clark’s theory of a Conceptual Pact, that when interlocutors agree on a name for an object, they are forming a temporary agreement on how to conceptualize that object, we present an extension to a simple reference resolver which simulates this process over time with different conversation pairs. In a puzzle construction domain, we model pacts with small language models for each referent which update during the interaction. When features from these pact models are incorporated into a simple bag-of-words reference resolver, the accuracy increases compared to using a standard pre-trained model. The model performs equally to a competitor using the same data but with exhaustive re-training after each prediction, while also being more transparent, faster and less resource-intensive. We also experiment with reducing the number of training interactions, and can still achieve reference resolution accuracies of over 80% in testing from observing a single previous interaction, over 20% higher than a pre-trained baseline. While this is a limited domain, we argue the model could be applicable to larger real-world applications in human and human-robot interaction and is an interpretable and transparent model.

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ConEC: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition
Ruizhe Huang | Mahsa Yarmohammadi | Jan Trmal | Jing Liu | Desh Raj | Leibny Paola Garcia | Alexei V. Ivanov | Patrick Ehlen | Mingzhi Yu | Dan Povey | Sanjeev Khudanpur

Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases — such as the speaker’s contacts or recent song playlists — during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus (“ConEC”) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants’ names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology

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Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition
Chengcheng Han | Renyu Zhu | Jun Kuang | Fengjiao Chen | Xiang Li | Ming Gao | Xuezhi Cao | Yunsen Xian

Prompt-based methods have been widely used in few-shot named entity recognition (NER). In this paper, we first conduct a preliminary experiment and observe that the key to affecting the performance of prompt-based NER models is the capability to detect entity boundaries. However, most existing models fail to boost such capability. To solve the issue, we propose a novel model, ParaBART, which consists of a BART encoder and a specially designed parabiotic decoder. Specifically, the parabiotic decoder includes two BART decoders and a conjoint module. The two decoders are responsible for entity boundary detection and entity type classification, respectively. They are connected by the conjoint module, which is used to replace unimportant tokens’ embeddings in one decoder with the average embedding of all the tokens in the other. We further present a novel boundary expansion strategy to enhance the model’s capability in entity type classification. Experimental results show that ParaBART can achieve significant performance gains over state-of-the-art competitors.

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CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English
Andrew Rueda | Elena Alvarez-Mellado | Constantine Lignos

Modern named entity recognition systems have steadily improved performance in the age of larger and more powerful neural models. However, over the past several years, the state-of-the-art has seemingly hit another plateau on the benchmark CoNLL-03 English dataset. In this paper, we perform a deep dive into the test outputs of the highest-performing NER models, conducting a fine-grained evaluation of their performance by introducing new document-level annotations on the test set. We go beyond F1 scores by categorizing errors in order to interpret the true state of the art for NER and guide future work. We review previous attempts at correcting the various flaws of the test set and introduce CoNLL#, a new corrected version of the test set that addresses its systematic and most prevalent errors, allowing for low-noise, interpretable error analysis.

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Connecting Language Technologies with Rich, Diverse Data Sources Covering Thousands of Languages
Daan van Esch | Sandy Ritchie | Sebastian Ruder | Julia Kreutzer | Clara Rivera | Ishank Saxena | Isaac Caswell

Contrary to common belief, there are rich and diverse data sources available for many thousands of languages, which can be used to develop technologies for these languages. In this paper, we provide an overview of some of the major online data sources, the types of data that they provide access to, potential applications of this data, and the number of languages that they cover. Even this covers only a small fraction of the data that exists; for example, printed books are published in many languages but few online aggregators exist.

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Constructing a Dependency Treebank for Second Language Learners of Korean
Hakyung Sung | Gyu-Ho Shin

We introduce a manually annotated syntactic treebank based on Universal Dependencies, derived from the written data of second language (L2) Korean learners. In developing this new dataset, we critically evaluated previous works and revised the annotation guidelines to better reflect the linguistic properties of Korean and the characteristics of L2 learners. The L2 Korean treebank encompasses 7,530 sentences (66,982 words; 129,333 morphemes) and is publicly available at: https://github.com/NLPxL2Korean/L2KW-corpus.

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Constructing Indonesian-English Travelogue Dataset
Eunike Andriani Kardinata | Hiroki Ouchi | Taro Watanabe

Research in low-resource language is often hampered due to the under-representation of how the language is being used in reality. This is particularly true for Indonesian language because there is a limited variety of textual datasets, and majority were acquired from official sources with formal writing style. All the more for the task of geoparsing, which could be implemented for navigation and travel planning applications, such datasets are rare, even in the high-resource languages, such as English. Being aware of the need for a new resource in both languages for this specific task, we constructed a new dataset comprising both Indonesian and English from personal travelogue articles. Our dataset consists of 88 articles, exactly half of them written in each language. We covered both named and nominal expressions of four entity types related to travel: location, facility, transportation, and line. We also conducted experiments by training classifiers to recognise named entities and their nominal expressions. The results of our experiments showed a promising future use of our dataset as we obtained F1-score above 0.9 for both languages.

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Constructing Korean Learners’ L2 Speech Corpus of Seven Languages for Automatic Pronunciation Assessment
Seunghee Han | Sunhee Kim | Minhwa Chung

Multilingual L2 speech corpora for developing automatic speech assessment are currently available, but they lack comprehensive annotations of L2 speech from non-native speakers of various languages. This study introduces the methodology of designing a Korean learners’ L2 speech corpus of seven languages: English, Japanese, Chinese, French, German, Spanish, and Russian. We describe the development of reading scripts, reading tasks, scoring criteria, and expert evaluation methods in detail. Our corpus contains 1,200 hours of L2 speech data from Korean learners (400 hours for English, 200 hours each for Japanese and Chinese, 100 hours each for French, German, Spanish, and Russian). The corpus is annotated with spelling and pronunciation transcription, expert pronunciation assessment scores (accuracy of pronunciation and fluency of prosody), and metadata such as gender, age, self-reported language proficiency, and pronunciation error types. We also propose a practical verification method and a reliability threshold to ensure the reliability and objectivity of large-scale subjective evaluation data.

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Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation
Ali Mousavi | Xin Zhan | He Bai | Peng Shi | Theodoros Rekatsinas | Benjamin Han | Yunyao Li | Jeffrey Pound | Joshua M. Susskind | Natalie Schluter | Ihab F. Ilyas | Navdeep Jaitly

Datasets that pair Knowledge Graphs (KG) and text together (KG-T) can be used to train forward and reverse neural models that generate text from KG and vice versa. However models trained on datasets where KG and text pairs are not equivalent can suffer from more hallucination and poorer recall. In this paper, we verify this empirically by generating datasets with different levels of noise and find that noisier datasets do indeed lead to more hallucination. We argue that the ability of forward and reverse models trained on a dataset to cyclically regenerate source KG or text is a proxy for the equivalence between the KG and the text in the dataset. Using cyclic evaluation we find that manually created WebNLG is much better than automatically created TeKGen and T-REx. Informed by these observations, we construct a new, improved dataset called LAGRANGE using heuristics meant to improve equivalence between KG and text and show the impact of each of the heuristics on cyclic evaluation. We also construct two synthetic datasets using large language models (LLMs), and observe that these are conducive to models that perform significantly well on cyclic generation of text, but less so on cyclic generation of KGs, probably because of a lack of a consistent underlying ontology.

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Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasons
Shijia Zhou | Leonie Weissweiler | Taiqi He | Hinrich Schütze | David R. Mortensen | Lori Levin

In this paper, we make a contribution that can be understood from two perspectives: from an NLP perspective, we introduce a small challenge dataset for NLI with large lexical overlap, which minimises the possibility of models discerning entailment solely based on token distinctions, and show that GPT-4 and Llama 2 fail it with strong bias. We then create further challenging sub-tasks in an effort to explain this failure. From a Computational Linguistics perspective, we identify a group of constructions with three classes of adjectives which cannot be distinguished by surface features. This enables us to probe for LLM’s understanding of these constructions in various ways, and we find that they fail in a variety of ways to distinguish between them, suggesting that they don’t adequately represent their meaning or capture the lexical properties of phrasal heads.

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Context-Aware Non-Autoregressive Document-Level Translation with Sentence-Aligned Connectionist Temporal Classification
Hao Yu | Kaiyu Huang | Anqi Zhao | Junpeng Liu | Degen Huang

Previous studies employ the autoregressive translation (AT) paradigm in the document-to-document neural machine translation. These methods extend the translation unit from a single sentence to a pseudo-document and encodes the full pseudo-document, avoiding the redundant computation problem in context. However, the AT methods cannot parallelize decoding and struggle with error accumulation, especially when the length of sentences increases. In this work, we propose a context-aware non-autoregressive framework with the sentence-aligned connectionist temporal classification (SA-CTC) loss for document-level neural machine translation. In particular, the SA-CTC loss reduces the search space of the decoding path by fixing the positions of the beginning and end tokens for each sentence in the document. Meanwhile, the context-aware architecture introduces preset nodes to represent sentence-level information and utilizes a hierarchical attention structure to regulate the attention hypothesis space. Experimental results show that our proposed method can achieve competitive performance compared with several strong baselines. Our method implements non-autoregressive modeling in Doc-to-Doc translation manner, achieving an average 46X decoding speedup compared to the document-level AT baselines on three benchmarks.

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Context Matters: Enhancing Metaphor Recognition in Proverbs
Gamze Goren | Carlo Strapparava

Despite the remarkable achievements of Large Language Models (LLMs) in various Natural Language Processing tasks, their competence in abstract language understanding remains a relatively under-explored territory. Figurative language interpretation serves as ideal testbed for assessing this as it requires models to navigate beyond the literal meaning and delve into underlying semantics of the figurative expressions. In this paper, we seek to examine the performance of GPT-3.5 in zero-shot setting through word-level metaphor detection. Specifically, we frame the task as annotation of word-level metaphors in proverbs. To this end, we employ a dataset of English proverbs and evaluated its performance by applying different prompting strategies. Our results show that the model shows a satisfactory performance at identifying word-level metaphors, particularly when it is prompted with a hypothetical context preceding the proverb. This observation underscores the pivotal role of well-designed prompts for zero-shot settings through which these models can be leveraged as annotators for subjective NLP tasks.

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Context Shapes Emergent Communication about Concepts at Different Levels of Abstraction
Kristina Kobrock | Xenia Isabel Ohmer | Elia Bruni | Nicole Gotzner

We study the communication of concepts at different levels of abstraction and in different contexts in an agent-based, interactive reference game. While playing the concept-level reference game, the neural network agents develop a communication system from scratch. We use a novel symbolic dataset that disentangles concept type (ranging from specific to generic) and context (ranging from fine to coarse) to study the influence of these factors on the emerging language. We compare two game scenarios: one in which speaker agents have access to context information (context-aware) and one in which the speaker agents do not have access to context information (context-unaware). First, we find that the agents learn higher-level concepts from the object inputs alone. Second, an analysis of the emergent communication system shows that only context-aware agents learn to communicate efficiently by adapting their messages to the context conditions and relying on context for unambiguous reference. Crucially, this behavior is not explicitly incentivized by the game, but efficient communication emerges and is driven by the availability of context alone. The emerging language we observe is reminiscent of evolutionary pressures on human languages and highlights the pivotal role of context in a communication system.

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Contextualizing Generated Citation Texts
Biswadip Mandal | Xiangci Li | Jessica Ouyang

Abstractive citation text generation is usually framed as an infilling task, where a sequence-to-sequence model is trained to generate a citation given a reference paper and the context window around the target; the generated citation should be a brief discussion of the reference paper as it relates to the citing context. However, examining a recent LED-based citation generation system, we find that many of the generated citations are generic summaries of the reference paper’s main contribution, ignoring the citation context’s focus on a different topic. To address this problem, we propose a simple modification to the citation text generation task: the generation target is not only the citation itself, but the entire context window, including the target citation. This approach can be easily applied to any abstractive citation generation system, and our experimental results show that training in this way is preferred by human readers and allows the generation model to make use of contextual clues about what topic to discuss and what stance to take.

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Contextual Modeling for Document-level ASR Error Correction
Jin Jiang | Xunjian Yin | Xiaojun Wan | Wei Peng | Rongjun Li | Jingyuan Yang | Yanquan Zhou

Contextual information, including the sentences in the same document and in other documents of the dataset, plays a crucial role in improving the accuracy of document-level ASR Error Correction (AEC), while most previous works ignore this. In this paper, we propose a context-aware method that utilizes a k-Nearest Neighbors (kNN) approach to enhance the AEC model by retrieving a datastore containing contextual information. We conduct experiments on two English and two Chinese datasets, and the results demonstrate that our proposed model can effectively utilize contextual information to improve document-level AEC. Furthermore, the context information from the whole dataset provides even better results.

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Continual Few-shot Event Detection via Hierarchical Augmentation Networks
Chenlong Zhang | Pengfei Cao | Yubo Chen | Kang Liu | Zhiqiang Zhang | Mengshu Sun | Jun Zhao

Traditional continual event detection relies on abundant labeled data for training, which is often impractical to obtain in real-world applications. In this paper, we introduce continual few-shot event detection (CFED), a more commonly encountered scenario when a substantial number of labeled samples are not accessible. The CFED task is challenging as it involves memorizing previous event types and learning new event types with few-shot samples. To mitigate these challenges, we propose a memory-based framework: Hierarchical Augmentation Network (HANet). To memorize previous event types with limited memory, we incorporate prototypical augmentation into the memory set. For the issue of learning new event types in few-shot scenarios, we propose a contrastive augmentation module for token representations. Despite comparing with previous state-of-the-art methods, we also conduct comparisons with ChatGPT. Experiment results demonstrate that our method significantly outperforms all of these methods in multiple continual few-shot event detection tasks.

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Continual Reinforcement Learning for Controlled Text Generation
Velizar Shulev | Khalil Sima’an

Controlled Text Generation (CTG) steers the generation of continuations of a given context (prompt) by a Large Language Model (LLM) towards texts possessing a given attribute (e.g., topic, sentiment). In this paper we view CTG as a Continual Learning problem: how to learn at every step to steer next-word generation, without having to wait for end-of-sentence. This continual view is useful for online applications such as CTG for speech, where end-of-sentence is often uncertain. We depart from an existing model, the Plug-and-Play language models (PPLM), which perturbs the context at each step to better predict next-words that posses the desired attribute. While PPLM is intricate and has many hyper-parameters, we provide a proof that the PPLM objective function can be reduced to a Continual Reinforcement Learning (CRL) reward function, thereby simplifying PPLM and endowing it with a better understood learning framework. Subsequently, we present, the first of its kind, CTG algorithm that is fully based on CRL and exhibit promising empirical results.

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Continued Pre-training on Sentence Analogies for Translation with Small Data
Liyan Wang | Haotong Wang | Yves Lepage

This paper introduces Continued Pre-training on Analogies (CPoA) to incorporate pre-trained language models with analogical abilities, aiming at improving performance in low-resource translations without data augmentation. We continue training the models on sentence analogies retrieved from a translation corpus. Considering the sparsity of analogy in corpora, especially in low-resource scenarios, we propose exploring approximate analogies between sentences. We attempt to find sentence analogies that might not conform to formal criteria for entire sentences but partial pieces. When training the models, we introduce a weighting scalar pertaining to the quality of analogies to adjust the influence: emphasizing closer analogies while diminishing the impact of far ones. We evaluate our approach on a low-resource translation task: German-Upper Sorbian. The results show that CPoA using 10 times fewer instances can effectively attain gains of +1.4 and +1.3 BLEU points over the original model in two translation directions. This improvement is more pronounced when there are fewer parallel examples.

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Continuous Relational Diffusion Driven Topic Model with Multi-grained Text for Microblog
Chenhao Wu | Ruifang He | Chang Liu | Bo Wang

Topic model is a statistical model that leverages unsupervised learning to mine hidden topics in document collections. The data sparsity and colloquialism of social texts make it difficult to accurately mine the topics. Traditional methods assume that there are only 0/1-state relationships between the two parties in the social networks, but the relationship status in real life is more complicated, such as continuously changing relationships with different degrees of intimacy. This paper proposes a continuous relational diffusion driven topic model (CRTM) with multi-grained text for microblog to realize the continuous representation of the relationship state and make up for the context and structural information lost by previous representation methods. Multi-grained text representation learning distinguishes the impact of formal and informal expression on the topics further and alleviates colloquialism problems. Specifically, based on the original social network, the reconstructed social network with continuous relationship status is obtained by using information diffusion technology. The graph convolution model is utilized to learn node embeddings through the new social network. Finally, the neural variational inference is applied to generate topics according to continuous relationships. We validate CRTM on three real datasets, and the experimental results show the effectiveness of the scheme.

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ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure
Mohamad Elzohbi | Richard Zhao

This paper presents ContrastWSD, a RoBERTa-based metaphor detection model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the contextual meaning with the basic meaning of a word to determine whether it is used metaphorically in a sentence. By utilizing the word senses derived from a WSD model, our model enhances the metaphor detection process and outperforms other methods that rely solely on contextual embeddings or integrate only the basic definitions and other external knowledge. We evaluate our approach on various benchmark datasets and compare it with strong baselines, indicating the effectiveness in advancing metaphor detection.

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Contribution of Move Structure to Automatic Genre Identification: An Annotated Corpus of French Tourism Websites
Rémi Cardon | Trang Tran Hanh Pham | Julien Zakhia Doueihi | Thomas François

The present work studies the contribution of move structure to automatic genre identification. This concept - well known in other branches of genre analysis - seems to have little application in natural language processing. We describe how we collect a corpus of websites in French related to tourism and annotate it with move structure. We conduct experiments on automatic genre identification with our corpus. Our results show that our approach for informing a model with move structure can increase its performance for automatic genre identification, and reduce the need for annotated data and computational power.

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Controllable Paraphrase Generation for Semantic and Lexical Similarities
Yuya Ogasa | Tomoyuki Kajiwara | Yuki Arase

We developed a controllable paraphrase generation model for semantic and lexical similarities using a simple and intuitive mechanism: attaching tags to specify these values at the head of the input sentence. Lexically diverse paraphrases have been long coveted for data augmentation. However, their generation is not straightforward because diversifying surfaces easily degrades semantic similarity. Furthermore, our experiments revealed two critical features in data augmentation by paraphrasing: appropriate similarities of paraphrases are highly downstream task-dependent, and mixing paraphrases of various similarities negatively affects the downstream tasks. These features indicated that the controllability in paraphrase generation is crucial for successful data augmentation. We tackled these challenges by fine-tuning a pre-trained sequence-to-sequence model employing tags that indicate the semantic and lexical similarities of synthetic paraphrases selected carefully based on the similarities. The resultant model could paraphrase an input sentence according to the tags specified. Extensive experiments on data augmentation for contrastive learning and pre-fine-tuning of pretrained masked language models confirmed the effectiveness of the proposed model. We release our paraphrase generation model and a corpus of 87 million diverse paraphrases. (https://github.com/Ogamon958/ConPGS)

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Controllable Sentence Simplification in Swedish Using Control Prefixes and Mined Paraphrases
Julius Monsen | Arne Jonsson

Making information accessible to diverse target audiences, including individuals with dyslexia and cognitive disabilities, is crucial. Automatic Text Simplification (ATS) systems aim to facilitate readability and comprehension by reducing linguistic complexity. However, they often lack customizability to specific user needs, and training data for smaller languages can be scarce. This paper addresses ATS in a Swedish context, using methods that provide more control over the simplification. A dataset of Swedish paraphrases is mined from large amounts of text and used to train ATS models utilizing prefix-tuning with control prefixes. We also introduce a novel data-driven method for selecting complexity attributes for controlling the simplification and compare it with previous approaches. Evaluation of the trained models using SARI and BLEU demonstrates significant improvements over the baseline — a fine-tuned Swedish BART model — and compared to previous Swedish ATS results. These findings highlight the effectiveness of employing paraphrase data in conjunction with controllable generation mechanisms for simplification. Additionally, the set of explored attributes yields similar results compared to previously used attributes, indicating their ability to capture important simplification aspects.

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Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction
Masahiro Kaneko | Naoaki Okazaki

In Grammatical Error Correction (GEC), it is crucial to ensure the user’s comprehension of a reason for correction. Existing studies present tokens, examples, and hints for corrections, but do not directly explain the reasons in natural language. Although methods that use Large Language Models (LLMs) to provide direct explanations in natural language have been proposed for various tasks, no such method exists for GEC. Generating explanations for GEC corrections involves aligning input and output tokens, identifying correction points, and presenting corresponding explanations consistently. However, it is not straightforward to specify a complex format to generate explanations, because explicit control of generation is difficult with prompts. This study introduces a method called controlled generation with Prompt Insertion (PI) so that LLMs can explain the reasons for corrections in natural language. In PI, LLMs first correct the input text, and then we automatically extract the correction points based on the rules. The extracted correction points are sequentially inserted into the LLM’s explanation output as prompts, guiding the LLMs to generate explanations for the correction points. We also create an Explainable GEC (XGEC) dataset of correction reasons by annotating NUCLE, CoNLL2013, and CoNLL2014. Although generations from GPT-3.5 and ChatGPT using original prompts miss some correction points, the generation control using PI can explicitly guide to describe explanations for all correction points, contributing to improved performance in generating correction reasons.

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ControversialQA: Exploring Controversy in Question Answering
Zhen Wang | Peide Zhu | Jie Yang

Controversy is widespread online. Previous studies mainly define controversy based on vague assumptions of its relation to sentiment such as hate speech and offensive words. This paper introduces the first question-answering dataset that defines content controversy by user perception, i.e., votes from plenty of users. It contains nearly 10K questions, and each question has a best answer and a most controversial answer. Experimental results reveal that controversy detection in question answering is essential and challenging, and there is no strong correlation between controversy and sentiment tasks. We also show that controversial answers and most acceptable answers cannot be distinguished by retrieval-based QA models, which may cause controversy issues. With these insights, we believe ControversialQA can inspire future research on controversy in QA systems.

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Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units
Biswesh Mohapatra | Seemab Hassan | Laurent Romary | Justine Cassell

Successful conversations often rest on common understanding, where all parties are on the same page about the information being shared. This process, known as conversational grounding, is crucial for building trustworthy dialog systems that can accurately keep track of and recall the shared information. The proficiencies of an agent in grounding the conveyed information significantly contribute to building a reliable dialog system. Despite recent advancements in dialog systems, there exists a noticeable deficit in their grounding capabilities. Traum (Traum, 1995) provided a framework for conversational grounding introducing Grounding Acts and Grounding Units, but substantial progress, especially in the realm of Large Language Models, remains lacking. To bridge this gap, we present the annotation of two dialog corpora employing Grounding Acts, Grounding Units, and a measure of their degree of grounding. We discuss our key findings during the annotation and also provide a baseline model to test the performance of current Language Models in categorizing the grounding acts of the dialogs. Our work aims to provide a useful resource for further research in making conversations with machines better understood and more reliable in natural day-to-day collaborative dialogs.

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Converting Legacy Data to CLDF: A FAIR Exit Strategy for Linguistic Web Apps
Robert Forkel | Daniel G. Swanson | Steven Moran

In the mid 2000s, there were several large-scale US National Science Foundation (NSF) grants awarded to projects aiming at developing digital infrastructure and standards for different forms of linguistics data. For example, MultiTree encoded language family trees as phylogenies in XML and LL-MAP converted detailed geographic maps of endangered languages into KML. As early stand-alone website applications, these projects allowed researchers interested in comparative linguistics to explore language genealogies and areality, respectively. However as time passed, the technologies that supported these web apps became deprecated, unsupported, and inaccessible. Here we take a future-oriented approach to digital obsolescence and illustrate how to convert legacy linguistic resources into FAIR data via the Cross-Linguistic Data Formats (CLDF). CLDF is built on the W3C recommendations Model for Tabular Data and Metadata on the Web and Metadata Vocabulary for Tabular Data developed by the CSVW (CSV on the Web) working group. Thus, each dataset is modeled as a set of tabular data files described by metadata in JSON. These standards and the tools built to validate and manipulate them provide an accessible and extensible format for converting legacy linguistic web apps into FAIR datasets.

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CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions
Donghee Choi | Mogan Gim | Donghyeon Park | Mujeen Sung | Hyunjae Kim | Jaewoo Kang | Jihun Choi

This paper introduces CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes, from which knowledge covering a broad range of aspects is acquired. CookingSense is constructed through a series of dictionary-based filtering and language model-based semantic filtering techniques, which results in a rich knowledgebase of multidisciplinary food-related assertions. Additionally, we present FoodBench, a novel benchmark to evaluate culinary decision support systems. From evaluations with FoodBench, we empirically prove that CookingSense improves the performance of retrieval augmented language models. We also validate the quality and variety of assertions in CookingSense through qualitative analysis.

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CoRelation: Boosting Automatic ICD Coding through Contextualized Code Relation Learning
Junyu Luo | Xiaochen Wang | Jiaqi Wang | Aofei Chang | Yaqing Wang | Fenglong Ma

Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of automatic ICD coding is modeling ICD code relations. However, current methods insufficiently model the intricate relationships among ICD codes and often overlook the importance of context in clinical notes. In this paper, we propose a novel approach, a contextualized and flexible framework, to enhance the learning of ICD code representations. Our approach, unlike existing methods, employs a dependent learning paradigm that considers the context of clinical notes in modeling all possible code relations. We evaluate our approach on six public ICD coding datasets and the experimental results demonstrate the effectiveness of our approach compared to state-of-the-art baselines.

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CORI: CJKV Benchmark with Romanization Integration - a Step towards Cross-lingual Transfer beyond Textual Scripts
Hoang Nguyen | Chenwei Zhang | Ye Liu | Natalie Parde | Eugene Rohrbaugh | Philip S. Yu

Naively assuming English as a source language may hinder cross-lingual transfer for many languages by failing to consider the importance of language contact. Some languages are more well-connected than others, and target languages can benefit from transferring from closely related languages; for many languages, the set of closely related languages does not include English. In this work, we study the impact of source language for cross-lingual transfer, demonstrating the importance of selecting source languages that have high contact with the target language. We also construct a novel benchmark dataset for close contact Chinese-Japanese-Korean-Vietnamese (CJKV) languages to further encourage in-depth studies of language contact. To comprehensively capture contact between these languages, we propose to integrate Romanized transcription beyond textual scripts via Contrastive Learning objectives, leading to enhanced cross-lingual representations and effective zero-shot cross-lingual transfer.

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Corpus Creation and Automatic Alignment of Historical Dutch Dialect Speech
Martijn Bentum | Eric Sanders | Antal P.J. van den Bosch | Douwe Zeldenrust | Henk van den Heuvel

The Dutch Dialect Database (also known as the ‘Nederlandse Dialectenbank’) contains dialectal variations of Dutch that were recorded all over the Netherlands in the second half of the twentieth century. A subset of these recordings of about 300 hours were enriched with manual orthographic transcriptions, using non-standard approximations of dialectal speech. In this paper we describe the creation of a corpus containing both the audio recordings and their corresponding transcriptions and focus on our method for aligning the recordings with the transcriptions and the metadata.

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Corpus Services: A Framework to Curate XML Corpus Data
Aleksandr Riaposov | Elena Lazarenko

This paper provides a comprehensive description of the Corpus Services framework—a collection of Java validation tools for language corpora compiled in XML-based data formats, in particular those using EXMARaLDA corpus software. Having successfully found application in several research projects, the core functionality of the framework is currently integrated in the automated curation and publication workflows for EXMARaLDA-driven corpora of Northern Eurasian languages, as developed by the long-term project INEL. Preliminary stages of development and examples of practical use cases are covered, a structured explanation of the framework’s current functionality and operational mechanisms is provided. Furthermore, the utilization of Corpus Services is extensively illustrated within the context of INEL workflows.

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Correcting Language Model Bias for Text Classification in True Zero-Shot Learning
Feng Zhao | Wan Xianlin | Cheng Yan | Chu Kiong Loo

Combining pre-trained language models (PLMs) and manual templates is a common practice for text classification in zero-shot scenarios. However, the effect of this approach is highly volatile, ranging from random guesses to near state-of-the-art results, depending on the quality of the manual templates. In this paper, we show that this instability stems from the fact that language models tend toward predicting certain label words of text classification, and manual templates can influence this tendency. To address this, we develop a novel pipeline for annotating and filtering a few examples from unlabeled examples. Moreover, we propose a new method to measure model bias on label words that utilizes unlabeled examples as a validation set when tuning language models. Our approach does not require any pre-labeled examples. Experimental results on six text classification tasks demonstrate that the proposed approach significantly outperforms standard prompt learning in zero-shot settings, achieving up to 19.7% absolute improvement and 13.8% average improvement. More surprisingly, on IMDB and SST-2, our approach even exceeds all few-shot baselines.

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Correcting Pronoun Homophones with Subtle Semantics in Chinese Speech Recognition
Zhaobo Zhang | Rui Gan | Pingpeng Yuan | Hai Jin

Speech recognition is becoming prevalent in daily life. However, due to the similar semantic context of the entities and the overlap of Chinese pronunciation, the pronoun homophone, especially “他/她/它 (he/she/it)”, (their pronunciation is “Tā”) is usually recognized incorrectly. It poses a challenge to automatically correct them during the post-processing of Chinese speech recognition. In this paper, we propose three models to address the common confusion issues in this domain, tailored to various application scenarios. We implement the language model, the LSTM model with semantic features, and the rule-based assisted Ngram model, enabling our models to adapt to a wide range of requirements, from high-precision to low-resource offline devices. The extensive experiments show that our models achieve the highest recognition rate for “Tā” correction with improvements from 70% in the popular voice input methods up to 90%. Further ablation analysis underscores the effectiveness of our models in enhancing recognition accuracy. Therefore, our models improve the overall experience of Chinese speech recognition of “Tā” and reduce the burden of manual transcription corrections.

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Correlations between Multilingual Language Model Geometry and Crosslingual Transfer Performance
Cheril Shah | Yashashree Chandak | Atharv Mahesh Mane | Benjamin Bergen | Tyler A. Chang

A common approach to interpreting multilingual language models is to evaluate their internal representations. For example, studies have found that languages occupy distinct subspaces in the models’ representation spaces, and geometric distances between languages often reflect linguistic properties such as language families and typological features. In our work, we investigate whether geometric distances between language representations correlate with zero-shot crosslingual transfer performance for POS-tagging and NER in three multilingual language models. We consider four distance metrics, including new metrics that identify a basis for a multilingual representation space that sorts axes based on their language-separability. We find that each distance metric either only moderately correlates or does not correlate with crosslingual transfer performance, and metrics do not generalize well across models, layers, and tasks. Although pairwise language separability is a reasonable predictor of crosslingual transfer, representational geometry overall is an inconsistent predictor for the crosslingual performance of multilingual language models.

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Cost-Effective Discourse Annotation in the Prague Czech–English Dependency Treebank
Jiří Mírovský | Pavlína Synková | Lucie Polakova | Marie Paclíková

We present a cost-effective method for obtaining a high-quality annotation of explicit discourse relations in the Czech part of the Prague Czech–English Dependency Treebank, a corpus of almost 50 thousand sentences coming from the Czech translation of the Wall Street Journal part of the Penn Treebank. We use three different sources of information and combine them to obtain the discourse annotation: (i) annotation projection from the Penn Discourse Treebank 3.0, (ii) manual tectogrammatical (deep syntax) representation of sentences of the corpus, and (iii) the Lexicon of Czech Discourse Connectives CzeDLex. After solving as many discrepancies as possible automatically, the final discourse annotation is achieved by manual inspection of the remaining problematic cases. The discourse annotation of the corpus will be available both in the Prague format (on top of tectogrammatical trees) with the Prague taxonomy of discourse types, and in the Penn format (on plain texts) with the Penn Discourse Treebank 3.0 sense taxonomy.

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Counterfactual Dialog Mixing as Data Augmentation for Task-Oriented Dialog Systems
Sebastian Steindl | Ulrich Schäfer | Bernd Ludwig

High-quality training data for Task-Oriented Dialog (TOD) systems is costly to come by if no corpora are available. One method to extend available data is data augmentation. Yet, the research into and adaptation of data augmentation techniques for TOD systems is limited in comparison with other data modalities. We propose a novel, causally-flavored data augmentation technique called Counterfactual Dialog Mixing (CDM) that generates realistic synthetic dialogs via counterfactuals to increase the amount of training data. We demonstrate the method on a benchmark dataset and show that a model trained to classify the counterfactuals from the original data fails to do so, which strengthens the claim of creating realistic synthetic dialogs. To evaluate the effectiveness of CDM, we train a current architecture on a benchmark dataset and compare the performance with and without CDM. By doing so, we achieve state-of-the-art on some metrics. We further investigate the external generalizability and a lower resource setting. To evaluate the models, we adopted an interactive evaluation scheme.

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Creating Terminological Resources in the Digital Age for Less-resourced Languages
Mercè Vàzquez

Multilingual terminological resources contain the most representative knowledge of specialized domains and allow professionals to create and translate specialized content in order to spread knowledge. Today, representative and useful multilingual terminological resources are available for the most resourced languages. This reduces or limits the development of knowledge in less-resourced languages across different specialized domains, mainly those that are constantly evolving and creating or adapting new concepts as needed. In this paper we present our methodology for carrying out terminological projects in Catalan, based entirely on open access linguistic resources and using natural language processing tools. The main objective of this research is to maximize the Catalan terminology currently available in open access, using a combination of natural language processing tools. The results are supervised by linguists and terminologist experts before being publicly available to the public. The findings of our research provide a new approach to terminology work, making it possible to design high-volume multilingual terminological projects that are manually revised by linguists and terminologists in the context of less-resourced languages.

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Creation and Analysis of an International Corpus of Privacy Laws
Sonu Gupta | Geetika Gopi | Harish Balaji | Ellen Poplavska | Nora O’Toole | Siddhant Arora | Thomas Norton | Norman Sadeh | Shomir Wilson

The landscape of privacy laws and regulations around the world is complex and ever-changing. National and super-national laws, agreements, decrees, and other government-issued rules form a patchwork that companies must follow to operate internationally. To examine the status and evolution of this patchwork, we introduce the Privacy Law Corpus, of 1,043 privacy laws, regulations, and guidelines, covering 183 jurisdictions. This corpus enables a large-scale quantitative and qualitative examination of legal focus on privacy. We examine the temporal distribution of when privacy laws were created and illustrate the dramatic increase in privacy legislation over the past 50 years, although a finer-grained examination reveals that the rate of increase varies depending on the personal data types that privacy laws address. Our exploration also demonstrates that most privacy laws respectively address relatively few personal data types. Additionally, topic modeling results show the prevalence of common themes in privacy laws, such as finance, healthcare, and telecommunications. Finally, we release the corpus to the research community to promote further study.

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Croatian Idioms Integration: Enhancing the LIdioms Multilingual Linked Idioms Dataset
Ivana Filipović Petrović | Miguel López Otal | Slobodan Beliga

Idioms, also referred to as phraseological units in some language terminologies, are a subset within the broader category of multi-word expressions. However, there is a lack of representation of idioms in Croatian, a low-resourced language, in the Linguistic Linked Open Data cloud (LLOD). To address this gap, we propose an extension of an existing RDF-based multilingual representation of idioms, referred to as the LIdioms dataset, which currently includes idioms from English, German, Italian, Portuguese, and Russian. This paper expands the existing resource by incorporating 1,042 Croatian idioms in an Ontolex Lemon format. In addition, to foster translation initiatives and facilitate intercultural exchange, these added Croatian idioms have also been linked to other idioms of the LIdioms dataset, with which they share similar meanings despite their differences in the expression aspect. This addition enriches the knowledge base of the LLOD community with a new language resource that includes Croatian idioms.

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CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization
Ruochen Zhang | Carsten Eickhoff

Cross-lingual summarization (CLS) has attracted increasing interest in recent years due to the availability of large-scale web-mined datasets and the advancements of multilingual language models. However, given the rareness of naturally occurring CLS resources, the majority of datasets are forced to rely on translation which can contain overly literal artifacts. This restricts our ability to observe naturally occurring CLS pairs that capture organic diction, including instances of code-switching. This alteration between languages in mid-message is a common phenomenon in multilingual settings yet has been largely overlooked in cross-lingual contexts due to data scarcity. To address this gap, we introduce CroCoSum, a dataset of cross-lingual code-switched summarization of technology news. It consists of over 24,000 English source articles and 18,000 human-written Chinese news summaries, with more than 92% of the summaries containing code-switched phrases. For reference, we evaluate the performance of existing approaches including pipeline, end-to-end, and zero-shot methods. We show that leveraging existing CLS resources as a pretraining step does not improve performance on CroCoSum, indicating the limited generalizability of current datasets. Finally, we discuss the challenges of evaluating cross-lingual summarizers on code-switched generation through qualitative error analyses.

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Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish
Recep Firat Cekinel | Çağrı Çöltekin | Pinar Karagoz

The rapid spread of misinformation through social media platforms has raised concerns regarding its impact on public opinion. While misinformation is prevalent in other languages, the majority of research in this field has concentrated on the English language. Hence, there is a scarcity of datasets for other languages, including Turkish. To address this concern, we have introduced the FCTR dataset, consisting of 3238 real-world claims. This dataset spans multiple domains and incorporates evidence collected from three Turkish fact-checking organizations. Additionally, we aim to assess the effectiveness of cross-lingual transfer learning for low-resource languages, with a particular focus on Turkish. We demonstrate in-context learning (zero-shot and few-shot) performance of large language models in this context. The experimental results indicate that the dataset has the potential to advance research in the Turkish language.

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Cross-lingual Named Entity Corpus for Slavic Languages
Jakub Piskorski | Michał Marcińczuk | Roman Yangarber

This paper presents a corpus manually annotated with named entities for six Slavic languages — Bulgarian, Czech, Polish, Slovenian, Russian, and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017–2023 as a part of the Workshops on Slavic Natural Language Processing. The corpus consists of 5,017 documents on seven topics. The documents are annotated with five classes of named entities. Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits — single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture with the pre-trained multilingual models — XLM-RoBERTa-large for named entity mention recognition and categorization, and mT5-large for named entity lemmatization and linking.

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Cross-Lingual NLU: Mitigating Language-Specific Impact in Embeddings Leveraging Adversarial Learning
Saedeh Tahery | Sahar Kianian | Saeed Farzi

Low-resource languages and computational expenses pose significant challenges in the domain of large language models (LLMs). Currently, researchers are actively involved in various efforts to tackle these challenges. Cross-lingual natural language processing (NLP) remains one of the most promising strategies to address these issues. In this paper, we introduce a novel approach that utilizes adversarial techniques to mitigate the impact of language-specific information in contextual embeddings generated by large multilingual language models, with potential applications in cross-lingual tasks. The study encompasses five different languages, including both Latin and non-Latin ones, in the context of two fundamental tasks in natural language understanding: intent detection and slot filling. The results primarily show that our current approach excels in zero-shot scenarios for Latin languages like Spanish. However, it encounters limitations when applied to languages distant from English, such as Thai and Persian. This highlights that while our approach effectively reduces the effect of language-specific information on the core meaning, it performs better for Latin languages that share language-specific nuances with English, as certain characteristics persist in the overall meaning within embeddings.

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Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity
Sho Hoshino | Akihiko Kato | Soichiro Murakami | Peinan Zhang

Learning better sentence embeddings leads to improved performance for natural language understanding tasks including semantic textual similarity (STS) and natural language inference (NLI). As prior studies leverage large-scale labeled NLI datasets for fine-tuning masked language models to yield sentence embeddings, task performance for languages other than English is often left behind. In this study, we directly compared two data augmentation techniques as potential solutions for monolingual STS: - (a): _cross-lingual transfer_ that exploits English resources alone as training data to yield non-English sentence embeddings as zero-shot inference, and - (b) _machine translation_ that coverts English data into pseudo non-English training data in advance. In our experiments on monolingual STS in Japanese and Korean, we find that the two data techniques yield performance on par. In addition, we find a superiority of Wikipedia domain over NLI domain as unlabeled training data for these languages. Combining our findings, we further demonstrate that the cross-lingual transfer of Wikipedia data exhibits improved performance.

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Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets
Shadi Manafi | Nikhil Krishnaswamy

Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities, or the ability to leverage information acquired in a source language and apply it to a target language. These capabilities find practical applications in well-established Natural Language Processing (NLP) tasks such as Named Entity Recognition (NER). This study aims to investigate the effectiveness of a source language when applied to a target language, particularly in the context of perturbing the input test set. We evaluate on 13 pairs of languages, each including one high-resource language (HRL) and one low-resource language (LRL) with a geographic, genetic, or borrowing relationship. We evaluate two well-known MLLMs—MBERT and XLM-R—on these pairs, in native LRL and cross-lingual transfer settings, in two tasks, under a set of different perturbations. Our findings indicate that NER cross-lingual transfer depends largely on the overlap of entity chunks. If a source and target language have more entities in common, the transfer ability is stronger. Models using cross-lingual transfer also appear to be somewhat more robust to certain perturbations of the input, perhaps indicating an ability to leverage stronger representations derived from the HRL. Our research provides valuable insights into cross-lingual transfer and its implications for NLP applications, and underscores the need to consider linguistic nuances and potential limitations when employing MLLMs across distinct languages.

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CrossTune: Black-Box Few-Shot Classification with Label Enhancement
Danqing Luo | Chen Zhang | Yan Zhang | Haizhou Li

Training or finetuning large-scale language models (LLMs) requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One approach is to treat these models as black boxes and use forward passes (Inference APIs) to interact with them. Current research focuses on adapting these black-box models to downstream tasks using gradient-free prompt optimization, but this often involves an expensive process of searching task-specific prompts. Therefore, we are motivated to study black-box language model adaptation without prompt search. Specifically, we introduce a label-enhanced cross-attention network called CrossTune, which models the semantic relatedness between the input text sequence and task-specific label descriptions. Its effectiveness is examined in the context of few-shot text classification. To improve the generalization of CrossTune, we utilize ChatGPT to generate additional training data through in-context learning. A switch mechanism is implemented to exclude low-quality ChatGPT-generated data. Through extensive experiments on seven benchmark text classification datasets, we demonstrate that our proposed approach outperforms the previous state-of-the-art gradient-free black-box tuning method by 5.7% on average. Even without using ChatGPT-augmented data, CrossTune performs better or comparably than previous black-box tuning methods, suggesting the effectiveness of our approach.

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Cross-type French Multiword Expression Identification with Pre-trained Masked Language Models
Van-Tuan Bui | Agata Savary

Multiword expressions (MWEs) pose difficulties for natural language processing (NLP) due to their linguistic features, such as syntactic and semantic properties, which distinguish them from regular word groupings. This paper describes a combination of two systems: one that learns verbal multiword expressions (VMWEs) and another that learns non-verbal MWEs (nVMWEs). Together, these systems leverage training data from both types of MWEs to enhance performance on a cross-type dataset containing both VMWEs and nVMWEs. Such scenarios emerge when datasets are developed using differing annotation schemes. We explore the fine-tuning of several state-of-the-art neural transformers for each MWE type. Our experiments demonstrate the advantages of the combined system over multi-task approaches or single-task models, addressing the challenges posed by diverse tagsets within the training data. Specifically, we evaluated the combined system on a French treebank named Sequoia, which features an annotation layer encompassing all syntactic types of French MWEs. With this combined approach, we improved the F1-score by approximately 3% on the Sequoia dataset.

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CSSWiki: A Chinese Sentence Simplification Dataset with Linguistic and Content Operations
Fengkai Liu | John S. Y. Lee

Sentence Simplification aims to make sentences easier to read and understand. With most effort on corpus development focused on English, the amount of annotated data is limited in Chinese. To address this need, we introduce CSSWiki, an open-source dataset for Chinese sentence simplification based on Wikipedia. This dataset contains 1.6k source sentences paired with their simplified versions. Each sentence pair is annotated with operation tags that distinguish between linguistic and content modifications. We analyze differences in annotation scheme and data statistics between CSSWiki and existing datasets. We then report baseline sentence simplification performance on CSSWiki using zero-shot and few-shot approaches with Large Language Models.

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CTSM: Combining Trait and State Emotions for Empathetic Response Model
Yufeng Wang | Chao Chen | Zhou Yang | Shuhui Wang | Xiangwen Liao

Empathetic response generation endeavors to empower dialogue systems to perceive speakers’ emotions and generate empathetic responses accordingly. Psychological research demonstrates that emotion, as an essential factor in empathy, encompasses trait emotions, which are static and context-independent, and state emotions, which are dynamic and context-dependent. However, previous studies treat them in isolation, leading to insufficient emotional perception of the context, and subsequently, less effective empathetic expression. To address this problem, we propose Combining Trait and State emotions for Empathetic Response Model (CTSM). Specifically, to sufficiently perceive emotions in dialogue, we first construct and encode trait and state emotion embeddings, and then we further enhance emotional perception capability through an emotion guidance module that guides emotion representation. In addition, we propose a cross-contrastive learning decoder to enhance the model’s empathetic expression capability by aligning trait and state emotions between generated responses and contexts. Both automatic and manual evaluation results demonstrate that CTSM outperforms state-of-the-art baselines and can generate more empathetic responses. Our code is available at https://github.com/wangyufeng-empty/CTSM

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CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages
Thuat Nguyen | Chien Van Nguyen | Viet Dac Lai | Hieu Man | Nghia Trung Ngo | Franck Dernoncourt | Ryan A. Rossi | Thien Huu Nguyen

Extensive training datasets represent one of the important factors for the impressive learning capabilities of large language models (LLMs). However, these training datasets for current LLMs, especially the recent state-of-the-art models, are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is released in Hugging Face facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX.

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Curation of Benchmark Templates for Measuring Gender Bias in Named Entity Recognition Models
Ana Cimitan | Ana Alves Pinto | Michaela Geierhos

Named Entity Recognition (NER) constitutes a popular machine learning technique that empowers several natural language processing applications. As with other machine learning applications, NER models have been shown to be susceptible to gender bias. The latter is often assessed using benchmark datasets, which in turn are curated specifically for a given Natural Language Processing (NLP) task. In this work, we investigate the robustness of benchmark templates to detect gender bias and propose a novel method to improve the curation of such datasets. The method, based on masked token prediction, aims to filter out benchmark templates with a higher probability of detecting gender bias in NER models. We tested the method for English and German, using the corresponding fine-tuned BERT base model (cased) as the NER model. The gender gaps detected with templates classified as appropriate by the method were statistically larger than those detected with inappropriate templates. The results were similar for both languages and support the use of the proposed method in the curation of templates designed to detect gender bias.

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CuRIAM: Corpus Re Interpretation and Metalanguage in U.S. Supreme Court Opinions
Michael Kranzlein | Nathan Schneider | Kevin Tobia

Most judicial decisions involve the interpretation of legal texts. As such, judicial opinions use language as the medium to comment on or draw attention to other language (for example, through definitions and hypotheticals about the meaning of a term from a statute). Language used this way is called metalanguage. Focusing on the U.S. Supreme Court, we view metalanguage as reflective of justices’ interpretive processes, bearing on current debates and theories about textualism in law and political science. As a step towards large-scale metalinguistic analysis with NLP, we identify 9 categories prominent in metalinguistic discussions, including key terms, definitions, and different kinds of sources. We annotate these concepts in a corpus of U.S. Supreme Court opinions. Our analysis of the corpus reveals high interannotator agreement, frequent use of quotes and sources, and several notable frequency differences between majority, concurring, and dissenting opinions. We observe fewer instances than expected of several legal interpretive categories. We discuss some of the challenges in developing the annotation schema and applying it and provide recommendations for how this corpus can be used for broader analyses.

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Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition
Cam-Van Thi Nguyen | Cao-Bach Nguyen | Duc-Trong Le | Quang-Thuy Ha

Emotion recognition in conversation (ERC) is a crucial task in natural language processing and affective computing. This paper proposes MultiDAG+CL, a novel approach for Multimodal Emotion Recognition in Conversation (ERC) that employs Directed Acyclic Graph (DAG) to integrate textual, acoustic, and visual features within a unified framework. The model is enhanced by Curriculum Learning (CL) to address challenges related to emotional shifts and data imbalance. Curriculum learning facilitates the learning process by gradually presenting training samples in a meaningful order, thereby improving the model’s performance in handling emotional variations and data imbalance. Experimental results on the IEMOCAP and MELD datasets demonstrate that the MultiDAG+CL models outperform baseline models. We release the code for and experiments: https://github.com/vanntc711/MultiDAG-CL.

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CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval
Santosh T.y.s.s. | Kristina Kaiser | Matthias Grabmair

In this paper, we introduce CuSINeS, a negative sampling approach to enhance the performance of Statutory Article Retrieval (SAR). CuSINeS offers three key contributions. Firstly, it employs a curriculum-based negative sampling strategy guiding the model to focus on easier negatives initially and progressively tackle more difficult ones. Secondly, it leverages the hierarchical and sequential information derived from the structural organization of statutes to evaluate the difficulty of samples. Lastly, it introduces a dynamic semantic difficulty assessment using the being-trained model itself, surpassing conventional static methods like BM25, adapting the negatives to the model’s evolving competence. Experimental results on a real-world expert-annotated SAR dataset validate the effectiveness of CuSINeS across four different baselines, demonstrating its versatility.

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CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling
Zheng Fang | Yulan He | Rob Procter

Most existing topic models rely on bag-of-words (BOW) representation, which limits their ability to capture word order information and leads to challenges with out-of-vocabulary (OOV) words in new documents. Contextualized word embeddings, however, show superiority in word sense disambiguation and effectively address the OOV issue. In this work, we introduce a novel neural topic model called the Contextlized Word Topic Model (CWTM), which integrates contextualized word embeddings from BERT. The model is capable of learning the topic vector of a document without BOW information. In addition, it can also derive the topic vectors for individual words within a document based on their contextualized word embeddings. Experiments across various datasets show that CWTM generates more coherent and meaningful topics compared to existing topic models, while also accommodating unseen words in newly encountered documents.

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CyberAgressionAdo-v2: Leveraging Pragmatic-Level Information to Decipher Online Hate in French Multiparty Chats
Anais Ollagnier

As a part of the release of the CyberAgressionAdo-V2 dataset, this paper introduces a new tagset that includes tags marking pragmatic-level information occurring in cyberbullying situations. The previous version of this dataset, CyberAgressionAdo-V1, consists of aggressive multiparty chats in French annotated using a hierarchical tagset developed to describe bullying narrative events including the participant roles, the presence of hate speech, the type of verbal abuse, among others. In contrast, CyberAgressionAdo-V2 uses a multi-label, fine-grained tagset marking the discursive role of exchanged messages as well as the context in which they occur — for instance, attack (ATK), defend (DFN), counterspeech (CNS), abet/instigate (AIN), gaslight (GSL), etc. This paper provides a comprehensive overview of the annotation tagset and presents statistical insights derived from its application. Additionally, we address the challenges encountered when annotating pragmatic-level information in this context, conducting a thorough analysis of annotator disagreements. The resulting dataset comprises 19 conversations that have been manually annotated and is now available to facilitate further research in the field.

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Czech Dataset for Complex Aspect-Based Sentiment Analysis Tasks
Jakub Šmíd | Pavel Přibáň | Ondrej Prazak | Pavel Kral

In this paper, we introduce a novel Czech dataset for aspect-based sentiment analysis (ABSA), which consists of 3.1K manually annotated reviews from the restaurant domain. The dataset is built upon the older Czech dataset, which contained only separate labels for the basic ABSA tasks such as aspect term extraction or aspect polarity detection. Unlike its predecessor, our new dataset is specifically designed to allow its usage for more complex tasks, e.g. target-aspect-category detection. These advanced tasks require a unified annotation format, seamlessly linking sentiment elements (labels) together. Our dataset follows the format of the well-known SemEval-2016 datasets. This design choice allows effortless application and evaluation in cross-lingual scenarios, ultimately fostering cross-language comparisons with equivalent counterpart datasets in other languages. The annotation process engaged two trained annotators, yielding an impressive inter-annotator agreement rate of approximately 90%. Additionally, we provide 24M reviews without annotations suitable for unsupervised learning. We present robust monolingual baseline results achieved with various Transformer-based models and insightful error analysis to supplement our contributions. Our code and dataset are freely available for non-commercial research purposes.

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DACL: Disfluency Augmented Curriculum Learning for Fluent Text Generation
Rohan Chaudhury | Maria Teleki | Xiangjue Dong | James Caverlee

Voice-driven software systems are in abundance. However, language models that power these systems are traditionally trained on fluent, written text corpora. Hence there can be a misalignment between the inherent disfluency of transcribed spoken content and the fluency of the written training data. Furthermore, gold-standard disfluency annotations of various complexities for incremental training can be expensive to collect. So, we propose in this paper a Disfluency Augmented Curriculum Learning (DACL) approach to tackle the complex structure of disfluent sentences and generate fluent texts from them, by using Curriculum Learning (CL) coupled with our synthetically augmented disfluent texts of various levels. DACL harnesses the tiered structure of our generated synthetic disfluent data using CL, by training the model on basic samples (i.e. more fluent) first before training it on more complex samples (i.e. more disfluent). In contrast to the random data exposure paradigm, DACL focuses on a simple-to-complex learning process. We comprehensively evaluate DACL on Switchboard Penn Treebank-3 and compare it to the state-of-the-art disfluency removal models. Our model surpasses existing techniques in word-based precision (by up to 1%) and has shown favorable recall and F1 scores.

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DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods
Lorenzo Lupo | Paul Bose | Mahyar Habibi | Dirk Hovy | Carlo Schwarz

Social scientists increasingly use demographically stratified social media data to study the attitudes, beliefs, and behavior of the general public. To facilitate such analyses, we construct, validate, and release publicly the representative DADIT dataset of 30M tweets of 20k Italian Twitter users, along with their bios and profile pictures. We enrich the user data with high-quality labels for gender, age, and location. DADIT enables us to train and compare the performance of various state-of-the-art models for the prediction of the gender and age of social media users. In particular, we investigate if tweets contain valuable information for the task, since popular classifiers like M3 don’t leverage them. Our best XLM-based classifier improves upon the commonly used competitor M3 by up to 53% F1. Especially for age prediction, classifiers profit from including tweets as features. We also confirm these findings on a German test set.

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DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition
Yi-Cheng Wang | Hsin-Wei Wang | Bi-Cheng Yan | Chi-Han Lin | Berlin Chen

End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks. A family of fast and lightweight named entity correction (NEC) models for ASR have recently been proposed, which normally build on pho-netic-level edit distance algorithms and have shown impressive NEC performance. However, as the named entity (NE) list grows, the problems of phonetic confusion in the NE list are exacerbated; for example, homophone ambiguities increase substantially. In view of this, we proposed a novel Description Augmented Named entity CorrEctoR (dubbed DANCER), which leverages entity descriptions to provide additional information to facilitate mitigation of phonetic con-fusion for NEC on ASR transcription. To this end, an efficient entity description augmented masked language model (EDA-MLM) comprised of a dense retrieval model is introduced, enabling MLM to adapt swiftly to domain-specific entities for the NEC task. A series of experiments conducted on the AISHELL-1 and Homophone datasets confirm the effectiveness of our modeling approach. DANCER outperforms a strong baseline, the phonetic edit-distance-based NEC model (PED-NEC), by a character error rate (CER) reduction of about 7% relatively on AISHELL-1 for named entities. More notably, when tested on Homophone that contain named entities of high phonetic confusion, DANCER offers a more pronounced CER reduction of 46% relatively over PED-NEC for named entities. The code is available at https://github.com/Amiannn/Dancer.

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DanteLLM: Let’s Push Italian LLM Research Forward!
Andrea Bacciu | Cesare Campagnano | Giovanni Trappolini | Fabrizio Silvestri

In recent years, the dominance of Large Language Models (LLMs) in the English language has become evident. However, there remains a pronounced gap in resources and evaluation tools tailored for non-English languages, underscoring a significant disparity in the global AI landscape. This paper seeks to bridge this gap, specifically focusing on the Italian linguistic context. We introduce a novel benchmark, and an open LLM Leaderboard, designed to evaluate LLMs’ performance in Italian, providing a rigorous framework for comparative analysis. In our assessment of currently available models, we highlight their respective strengths and limitations against this standard. Crucially, we propose “DanteLLM”, a state-of-the-art LLM dedicated to Italian. Our empirical evaluations underscore Dante’s superiority, as it emerges as the most performant model on our benchmark, with improvements by up to 6 points. This research not only marks a significant stride in Italian-centric natural language processing but also offers a blueprint for the development and evaluation of LLMs in other languages, championing a more inclusive AI paradigm. Our code at: https://github.com/RSTLess-research/DanteLLM

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DARIUS: A Comprehensive Learner Corpus for Argument Mining in German-Language Essays
Nils-Jonathan Schaller | Andrea Horbach | Lars Ingver Höft | Yuning Ding | Jan Luca Bahr | Jennifer Meyer | Thorben Jansen

In this paper, we present the DARIUS (Digital Argumentation Instruction for Science) corpus for argumentation quality on 4589 essays written by 1839 German secondary school students. The corpus is annotated according to a fine-grained annotation scheme, ranging from a broader perspective like content zones, to more granular features like argumentation coverage/reach and argumentative discourse units like claims and warrants. The features have inter-annotator agreements up to 0.83 Krippendorff’s α. The corpus and dataset are publicly available for further research in argument mining.

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Data Collection Pipeline for Low-Resource Languages: A Case Study on Constructing a Tetun Text Corpus
Gabriel de Jesus | Sérgio Sobral Nunes

This paper proposes Labadain Crawler, a data collection pipeline tailored to automate and optimize the process of constructing textual corpora from the web, with a specific target to low-resource languages. The system is built on top of Nutch, an open-source web crawler and data extraction framework, and incorporates language processing components such as a tokenizer and a language identification model. The pipeline efficacy is demonstrated through successful testing with Tetun, one of Timor-Leste’s official languages, resulting in the construction of a high-quality Tetun text corpus comprising 321.7k sentences extracted from over 22k web pages. The contributions of this paper include the development of a Tetun tokenizer, a Tetun language identification model, and a Tetun text corpus, marking an important milestone in Tetun text information retrieval.

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Data Drift in Clinical Outcome Prediction from Admission Notes
Paul Grundmann | Jens-Michalis Papaioannou | Tom Oberhauser | Thomas Steffek | Amy Siu | Wolfgang Nejdl | Alexander Loeser

Clinical NLP research faces a scarcity of publicly available datasets due to privacy concerns. MIMIC-III marked a significant milestone, enabling substantial progress, and now, with MIMIC-IV, the dataset has expanded significantly, offering a broader scope. In this paper, we focus on the task of predicting clinical outcomes from clinical text. This is crucial in modern healthcare, aiding in preventive care, differential diagnosis, and capacity planning. We introduce a novel clinical outcome prediction dataset derived from MIMIC-IV. Furthermore, we provide initial insights into the performance of models trained on MIMIC-III when applied to our new dataset, with specific attention to potential data drift. We investigate challenges tied to evolving documentation standards and changing codes in the International Classification of Diseases (ICD) taxonomy, such as the transition from ICD-9 to ICD-10. We also explore variations in clinical text across different hospital wards. Our study aims to probe the robustness and generalization of clinical outcome prediction models, contributing to the ongoing advancement of clinical NLP in healthcare.

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Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks
Ileana Rugina | Rumen Dangovski | Li Jing | Preslav Nakov | Marin Soljacic

Attention mechanisms play a crucial role in the neural revolution of Natural Language Processing (NLP). With the growth of attention-based models, several pruning techniques have been developed to identify and exploit sparseness, making these models more efficient. Most efforts focus on hard-coding attention patterns or pruning attention weights based on training data. We propose Attention Pruning (AP), a framework that observes attention patterns in a fixed dataset and generates a global sparseness mask. AP saves 90% of attention computation for language modeling and about 50% for machine translation and GLUE tasks, maintaining result quality. Our method reveals important distinctions between self- and cross-attention patterns, guiding future NLP research. Our framework can reduce both latency and memory requirements for any attention-based model, aiding in the development of improved models for existing or new NLP applications. We have demonstrated this with encoder and autoregressive transformer models using Triton GPU kernels and make our code publicly available at https://github.com/irugina/AP

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Dataset for Identification of Homophobia and Transphobia for Telugu, Kannada, and Gujarati
Prasanna Kumar Kumaresan | Rahul Ponnusamy | Dhruv Sharma | Paul Buitelaar | Bharathi Raja Chakravarthi

Users of social media platforms are negatively affected by the proliferation of hate or abusive content. There has been a rise in homophobic and transphobic content in recent years targeting LGBT+ individuals. The increasing levels of homophobia and transphobia online can make online platforms harmful and threatening for LGBT+ persons, potentially inhibiting equality, diversity, and inclusion. We are introducing a new dataset for three languages, namely Telugu, Kannada, and Gujarati. Additionally, we have created an expert-labeled dataset to automatically identify homophobic and transphobic content within comments collected from YouTube. We provided comprehensive annotation rules to educate annotators in this process. We collected approximately 10,000 comments from YouTube for all three languages. Marking the first dataset of these languages for this task, we also developed a baseline model with pre-trained transformers.

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Dataset of Quotation Attribution in German News Articles
Fynn Petersen-Frey | Chris Biemann

Extracting who says what to whom is a crucial part in analyzing human communication in today’s abundance of data such as online news articles. Yet, the lack of annotated data for this task in German news articles severely limits the quality and usability of possible systems. To remedy this, we present a new, freely available, creative-commons-licensed dataset for quotation attribution in German news articles based on WIKINEWS. The dataset provides curated, high-quality annotations across 1000 documents (250,000 tokens) in a fine-grained annotation schema enabling various downstream uses for the dataset. The annotations not only specify who said what but also how, in which context, to whom and define the type of quotation. We specify our annotation schema, describe the creation of the dataset and provide a quantitative analysis. Further, we describe suitable evaluation metrics, apply two existing systems for quotation attribution, discuss their results to evaluate the utility of our dataset and outline use cases of our dataset in downstream tasks.

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DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding
Jianhao Yan | Jin Xu | Fandong Meng | Jie Zhou | Yue Zhang

Minimum Bayesian Risk Decoding (MBR) emerges as a promising decoding algorithm in Neural Machine Translation. However, MBR performs poorly with label smoothing, which is surprising as label smoothing provides decent improvement with beam search and improves generality in various tasks. In this work, we show that the issue arises from the inconsistency of label smoothing on the token-level and sequence-level distributions. We demonstrate that even though label smoothing only causes a slight change in the token level, the sequence-level distribution is highly skewed. We coin the issue autoregressive over-smoothness. To address this issue, we propose a simple and effective method, Distributional Cooling MBR (DC-MBR), which manipulates the entropy of output distributions by tuning down the Softmax temperature. We theoretically prove the equivalence between the pre-tuning label smoothing factor and distributional cooling. Extensive experiments on NMT benchmarks validate that distributional cooling improves MBR in various settings.

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DDxGym: Online Transformer Policies in a Knowledge Graph Based Natural Language Environment
Benjamin Winter | Alexei Gustavo Figueroa Rosero | Alexander Loeser | Felix Alexander Gers | Nancy Katerina Figueroa Rosero | Ralf Krestel

Differential diagnosis (DDx) is vital for physicians and challenging due to the existence of numerous diseases and their complex symptoms. Model training for this task is generally hindered by limited data access due to privacy concerns. To address this, we present DDxGym, a specialized OpenAI Gym environment for clinical differential diagnosis. DDxGym formulates DDx as a natural-language-based reinforcement learning (RL) problem, where agents emulate medical professionals, selecting examinations and treatments for patients with randomly sampled diseases. This RL environment utilizes data labeled from online resources, evaluated by medical professionals for accuracy. Transformers, while effective for encoding text in DDxGym, are unstable in online RL. For that reason we propose a novel training method using an auxiliary masked language modeling objective for policy optimization, resulting in model stabilization and significant performance improvement over strong baselines. Following this approach, our agent effectively navigates large action spaces and identifies universally applicable actions. All data, environment details, and implementation, including experiment reproduction code, are made publicly available.

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Dealing with Data Scarcity in Spoken Question Answering
Merve Ünlü Menevşe | Yusufcan Manav | Ebru Arisoy | Arzucan Özgür

This paper focuses on dealing with data scarcity in spoken question answering (QA) using automatic question-answer generation and a carefully selected fine-tuning strategy that leverages limited annotated data (paragraphs and question-answer pairs). Spoken QA is a challenging task due to using spoken documents, i.e., erroneous automatic speech recognition (ASR) transcriptions, and the scarcity of spoken QA data. We propose a framework for utilizing limited annotated data effectively to improve spoken QA performance. To deal with data scarcity, we train a question-answer generation model with annotated data and then produce large amounts of question-answer pairs from unannotated data (paragraphs). Our experiments demonstrate that incorporating limited annotated data and the automatically generated data through a carefully selected fine-tuning strategy leads to 5.5% relative F1 gain over the model trained only with annotated data. Moreover, the proposed framework is also effective in high ASR errors.

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Debiasing Multi-Entity Aspect-Based Sentiment Analysis with Norm-Based Data Augmentation
Scott Friedman | Joan Zheng | Hillel Steinmetz

Bias in NLP models may arise from using pre-trained transformer models trained on biased corpora, or by training or fine-tuning directly on corpora with systemic biases. Recent research has explored strategies for reduce measurable biases in NLP predictions while maintaining prediction accuracy on held-out test sets, e.g., by modifying word embedding geometry after training, using purpose-built neural modules for training, or automatically augmenting training data with examples designed to reduce bias. This paper focuses on a debiasing strategy for aspect-based sentiment analysis (ABSA) by augmenting the training data using norm-based language templates derived from previous language resources. We show that the baseline model predicts lower sentiment toward some topics and individuals than others and has relatively high prediction bias (measured by standard deviation), even when the context is held constant. Our results show that our norm-based data augmentation reduces topical bias to less than half while maintaining prediction quality (measured by RMSE), by augmenting the training data by only 1.8%.

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Deciphering Emotional Landscapes in the Iliad: A Novel French-Annotated Dataset for Emotion Recognition
Davide Picca | John Pavlopoulos

One of the most significant pieces of ancient Greek literature, the Iliad, is part of humanity’s collective cultural heritage. This work aims to provide the scientific community with an emotion-labeled dataset for classical literature and Western mythology in particular. To model the emotions of the poem, we use a multi-variate time series. We also evaluated the dataset by means of two methods. We compare the manual classification against a dictionary-based benchmark as well as employ a state-of-the-art deep learning masked language model that has been tuned using our data. Both evaluations return encouraging results (MSE and MAE Macro Avg 0.101 and 0.188 respectively) and highlight some interesting phenomena.

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DECM: Evaluating Bilingual ASR Performance on a Code-switching/mixing Benchmark
Enes Yavuz Ugan | Ngoc-Quan Pham | Alexander Waibel

Automatic Speech Recognition has made significant progress, but challenges persist. Code-switched (CSW) Speech presents one such challenge, involving the mixing of multiple languages by a speaker. Even when multilingual ASR models are trained, each utterance on its own usually remains monolingual. We introduce an evaluation dataset for German-English CSW, with German as the matrix language and English as the embedded language. The dataset comprises spontaneous speech from diverse domains, enabling realistic CSW evaluation in German-English. It includes splits with varying degrees of CSW to facilitate specialized model analysis. As it is difficult to collect CSW data for all language pairs, the provision of such evaluation data, is crucial for developing and analyzing ASR models capable of generalizing across unseen pairs. Detailed data statistics are presented, and state-of-the-art (SOTA) multilingual models are evaluated showing challanges of CSW speech.

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Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs
Chenxi Sun | Hongzhi Zhang | Zijia Lin | Jingyuan Zhang | Fuzheng Zhang | Zhongyuan Wang | Bin Chen | Chengru Song | Di Zhang | Kun Gai | Deyi Xiong

Large language models have demonstrated exceptional capability in natural language understanding and generation. However, their generation speed is limited by the inherently sequential nature of their decoding process, posing challenges for real-time applications. This paper introduces Lexical Unit Decoding (LUD), a novel decoding methodology implemented in a data-driven manner, accelerating the decoding process without sacrificing output quality. The core of our approach is the observation that a pre-trained language model can confidently predict multiple contiguous tokens, forming the basis for a lexical unit, in which these contiguous tokens could be decoded in parallel. Extensive experiments validate that our method substantially reduces decoding time while maintaining generation quality, i.e., 33% speed up on natural language generation with no quality loss, and 30% speed up on code generation with a negligible quality loss of 3%. Distinctively, LUD requires no auxiliary models and does not require changes to existing architectures. It can also be integrated with other decoding acceleration methods, thus achieving an even more pronounced inference efficiency boost. We posit that the foundational principles of LUD could define a new decoding paradigm for future language models, enhancing their applicability for a broader spectrum of applications. All codes are be publicly available at https://github.com/tjunlp-lab/Lexical-Unit-Decoding-LUD-.

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Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models Using Minimal Pairs
Linyang He | Peili Chen | Ercong Nie | Yuanning Li | Jonathan R. Brennan

Inspired by cognitive neuroscience studies, we introduce a novel “decoding probing” method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in neural language models layer by layer. By treating the language model as the brain and its representations as “neural activations”, we decode grammaticality labels of minimal pairs from the intermediate layers’ representations. This approach reveals: 1) Self-supervised language models capture abstract linguistic structures in intermediate layers that GloVe and RNN language models cannot learn. 2) Information about syntactic grammaticality is robustly captured through the first third layers of GPT-2 and also distributed in later layers. As sentence complexity increases, more layers are required for learning grammatical capabilities. 3) Morphological and semantics/syntax interface-related features are harder to capture than syntax. 4) For Transformer-based models, both embeddings and attentions capture grammatical features but show distinct patterns. Different attention heads exhibit similar tendencies toward various linguistic phenomena, but with varied contributions.

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Decompose, Prioritize, and Eliminate: Dynamically Integrating Diverse Representations for Multimodal Named Entity Recognition
Zihao Zheng | Zihan Zhang | Zexin Wang | Ruiji Fu | Ming Liu | Zhongyuan Wang | Bing Qin

Multi-modal Named Entity Recognition, a fundamental task for multi-modal knowledge graph construction, requires integrating multi-modal information to extract named entities from text. Previous research has explored the integration of multi-modal representations at different granularities. However, they struggle to integrate all these multi-modal representations to provide rich contextual information to improve multi-modal named entity recognition. In this paper, we propose DPE-MNER, which is an iterative reasoning framework that dynamically incorporates all the diverse multi-modal representations following the strategy of “decompose, prioritize, and eliminate”. Within the framework, the fusion of diverse multi-modal representations is decomposed into hierarchically connected fusion layers that are easier to handle. The incorporation of multi-modal information prioritizes transitioning from “easy-to-hard” and “coarse-to-fine”. The explicit modeling of cross-modal relevance eliminate the irrelevances that will mislead the MNER prediction. Extensive experiments on two public datasets have demonstrated the effectiveness of our approach.

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Deconstructing In-Context Learning: Understanding Prompts via Corruption
Namrata Shivagunde | Vladislav Lialin | Sherin Muckatira | Anna Rumshisky

The ability of large language models (LLMs) to “learn in context” based on the provided prompt has led to an explosive growth in their use, culminating in the proliferation of AI assistants such as ChatGPT, Claude, and Bard. These AI assistants are known to be robust to minor prompt modifications, mostly due to alignment techniques that use human feedback. In contrast, the underlying pre-trained LLMs they use as a backbone are known to be brittle in this respect. Building high-quality backbone models remains a core challenge, and a common approach to assessing their quality is to conduct few-shot evaluation. Such evaluation is notorious for being highly sensitive to minor prompt modifications, as well as the choice of specific in-context examples. Prior work has examined how modifying different elements of the prompt can affect model performance. However, these earlier studies tended to concentrate on a limited number of specific prompt attributes and often produced contradictory results. Additionally, previous research either focused on models with fewer than 15 billion parameters or exclusively examined black-box models like GPT-3 or PaLM, making replication challenging. In the present study, we decompose the entire prompt into four components: task description, demonstration inputs, labels, and inline instructions provided for each demonstration. We investigate the effects of structural and semantic corruptions of these elements on model performance. We study models ranging from 1.5B to 70B in size, using ten datasets covering classification and generation tasks. We find that repeating text within the prompt boosts model performance, and bigger models (≥30B) are more sensitive to the semantics of the prompt. Finally, we observe that adding task and inline instructions to the demonstrations enhances model performance even when the instructions are semantically corrupted. The code is available at this URL.

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DEEM: Dynamic Experienced Expert Modeling for Stance Detection
Xiaolong Wang | Yile Wang | Sijie Cheng | Peng Li | Yang Liu

Recent work has made a preliminary attempt to use large language models (LLMs) to solve the stance detection task, showing promising results. However, considering that stance detection usually requires detailed background knowledge, the vanilla reasoning method may neglect the domain knowledge to make a professional and accurate analysis. Thus, there is still room for improvement of LLMs reasoning, especially in leveraging the generation capability of LLMs to simulate specific experts (i.e., multi-agents) to detect the stance. In this paper, different from existing multi-agent works that require detailed descriptions and use fixed experts, we propose a Dynamic Experienced Expert Modeling (DEEM) method which can leverage the generated experienced experts and let LLMs reason in a semi-parametric way, making the experts more generalizable and reliable. Experimental results demonstrate that DEEM consistently achieves the best results on three standard benchmarks, outperforms methods with self-consistency reasoning, and reduces the bias of LLMs.

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Deep Learning Based Named Entity Recognition Models for Recipes
Ayush Agarwal | Janak Kapuriya | Shubham Agrawal | Akhil Vamshi Konam | Mansi Goel | Rishabh Gupta | Shrey Rastogi | Niharika Niharika | Ganesh Bagler

Food touches our lives through various endeavors, including flavor, nourishment, health, and sustainability. Recipes are cultural capsules transmitted across generations via unstructured text. Automated protocols for recognizing named entities, the building blocks of recipe text, are of immense value for various applications ranging from information extraction to novel recipe generation. Named entity recognition is a technique for extracting information from unstructured or semi-structured data with known labels. Starting with manually-annotated data of 6,611 ingredient phrases, we created an augmented dataset of 26,445 phrases cumulatively. Simultaneously, we systematically cleaned and analyzed ingredient phrases from RecipeDB, the gold-standard recipe data repository, and annotated them using the Stanford NER. Based on the analysis, we sampled a subset of 88,526 phrases using a clustering-based approach while preserving the diversity to create the machine-annotated dataset. A thorough investigation of NER approaches on these three datasets involving statistical, fine-tuning of deep learning-based language models and few-shot prompting on large language models (LLMs) provides deep insights. We conclude that few-shot prompting on LLMs has abysmal performance, whereas the fine-tuned spaCy-transformer emerges as the best model with macro-F1 scores of 95.9%, 96.04%, and 95.71% for the manually-annotated, augmented, and machine-annotated datasets, respectively.

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Deep Reinforcement Learning-based Dialogue Policy with Graph Convolutional Q-network
Kai Xu | Zhengyu Wang | Yuxuan Long | Qiaona Zhao

Deep Reinforcement learning (DRL) has been successfully applied to the dialogue policy of task-oriented dialogue systems. However, one challenge in the existing DRL-based dialogue policy methods is their unstructured state-action representations without the ability to learn the relationship between dialogue states and actions. To alleviate this problem, we propose a graph-structured dialogue policy framework for task-oriented dialogue systems. More specifically, we use an unsupervised approach to construct two different bipartite graphs. Then, we generate the user-related and knowledge-related subgraphs based on the matching dialogue sub-states with bipartite graph nodes. A variant of graph convolutional network is employed to encode dialogue subgraphs. After that, we use a bidirectional gated cycle unit (BGRU) and self-attention mechanism to obtain the high-level historical state representations and employ a neural network for the high-level current state representations. The two state representations are joined to learn the action value of dialogue policy. Experiments implemented with different DRL algorithms demonstrate that the proposed framework significantly improves the effectiveness and stability of dialogue policies.

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Deep Reinforcement Learning with Hierarchical Action Exploration for Dialogue Generation
Itsugun Cho | Ryota Takahashi | Yusaku Yanase | Hiroaki Saito

Traditionally, approximate dynamic programming is employed in dialogue generation with greedy policy improvement through action sampling, as the natural language action space is vast. However, this practice is inefficient for reinforcement learning (RL) due to the sparsity of eligible responses with high action values, which leads to weak improvement sustained by random sampling. This paper presents theoretical analysis and experiments that reveal the performance of the dialogue policy is positively correlated with the sampling size. To overcome this limitation, we introduce a novel dual-granularity Q-function that explores the most promising response category to intervene in the sampling process. Our approach extracts actions based on a grained hierarchy, thereby achieving the optimum with fewer policy iterations. Additionally, we use offline RL and learn from multiple reward functions designed to capture emotional nuances in human interactions. Empirical studies demonstrate that our algorithm outperforms baselines across automatic metrics and human evaluations. Further testing reveals that our algorithm exhibits both explainability and controllability, as well as generates responses with higher expected rewards.

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DeFaktS: A German Dataset for Fine-Grained Disinformation Detection through Social Media Framing
Shaina Ashraf | Isabel Bezzaoui | Ionut Andone | Alexander Markowetz | Jonas Fegert | Lucie Flek

In today’s rapidly evolving digital age, disinformation poses a significant threat to public sentiment and socio-political dynamics. To address this, we introduce a new dataset “DeFaktS”, designed to understand and counter disinformation within German media. Distinctively curated across various news topics, DeFaktS offers an unparalleled insight into the diverse facets of disinformation. Our dataset, containing 105,855 posts with 20,008 meticulously labeled tweets, serves as a rich platform for in-depth exploration of disinformation’s diverse characteristics. A key attribute that sets DeFaktS apart is, its fine-grain annotations based on polarized categories. Our annotation framework, grounded in the textual characteristics of news content, eliminates the need for external knowledge sources. Unlike most existing corpora that typically assign a singular global veracity value to news, our methodology seeks to annotate every structural component and semantic element of a news piece, ensuring a comprehensive and detailed understanding. In our experiments, we employed a mix of classical machine learning and advanced transformer-based models. The results underscored the potential of DeFaktS, with transformer models, especially the German variant of BERT, exhibiting pronounced effectiveness in both binary and fine-grained classifications.

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DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset
Yubing Ren | Yanan Cao | Hao Li | Yingjie Li | Zixuan ZM Ma | Fang Fang | Ping Guo | Wei Ma

A text corpus centered on events is foundational to research concerning the detection, representation, reasoning, and harnessing of online events. The majority of current event-based datasets mainly target sentence-level tasks, thus to advance event-related research spanning from sentence to document level, this paper introduces DEIE, a unified large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments. Three key features stand out: large-scale manual annotation (20,000 documents), comprehensive unified annotation (encompassing event trigger/argument, summary, and relation at once), and emergency events annotation (covering 19 emergency types). Notably, our experiments reveal that current event-related models struggle with DEIE, signaling a pressing need for more advanced event-related research in the future.

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DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning
Mengfei Du | Binhao Wu | Jiwen Zhang | Zhihao Fan | Zejun Li | Ruipu Luo | Xuanjing Huang | Zhongyu Wei

Vision-and-Language navigation (VLN) requires an agent to navigate in unseen environment by following natural language instruction. For task completion, the agent needs to align and integrate various navigation modalities, including instruction, observation and navigation history. Existing works primarily concentrate on cross-modal attention at the fusion stage to achieve this objective. Nevertheless, modality features generated by disparate uni-encoders reside in their own spaces, leading to a decline in the quality of cross-modal fusion and decision. To address this problem, we propose a Dual-levEL AligNment (DELAN) framework by cross-modal contrastive learning. This framework is designed to align various navigation-related modalities before fusion, thereby enhancing cross-modal interaction and action decision-making. Specifically, we divide the pre-fusion alignment into dual levels: instruction-history level and landmark-observation level according to their semantic correlations. We also reconstruct a dual-level instruction for adaptation to the dual-level alignment. As the training signals for pre-fusion alignment are extremely limited, self-supervised contrastive learning strategies are employed to enforce the matching between different modalities. Our approach seamlessly integrates with the majority of existing models, resulting in improved navigation performance on various VLN benchmarks, including R2R, R4R, RxR and CVDN.

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Demonstration Retrieval-Augmented Generative Event Argument Extraction
Shiming He | Yu Hong | Shuai Yang | Jianmin Yao | Guodong Zhou

We tackle Event Argument Extraction (EAE) in the manner of template-based generation. Based on our exploration of generative EAE, it suffers from several issues, such as multiple arguments of one role, generating words out of context and inconsistency with prescribed format. We attribute it to the weakness of following complex input prompts. To address these problems, we propose the demonstration retrieval-augmented generative EAE (DRAGEAE), containing two components: event knowledge-injected generator (EKG) and demonstration retriever (DR). EKG employs event knowledge prompts to capture role dependencies and semantics. DR aims to search informative demonstrations from training data, facilitating the conditional generation of EKG. To train DR, we use the probability-based rankings from large language models (LLMs) as supervised signals. Experimental results on ACE-2005, RAMS and WIKIEVENTS demonstrate that our method outperforms all strong baselines and it can be generalized to various datasets. Further analysis is conducted to discuss the impact of diverse LLMs and prove that our model alleviates the above issues.

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Denoising Labeled Data for Comment Moderation Using Active Learning
Andraž Pelicon | Mladen Karan | Ravi Shekhar | Matthew Purver | Senja Pollak

Noisily labeled textual data is ample on internet platforms that allow user-created content. Training models, such as offensive language detection models for comment moderation, on such data may prove difficult as the noise in the labels prevents the model to converge. In this work, we propose to use active learning methods for the purposes of denoising training data for model training. The goal is to sample examples the most informative examples with noisy labels with active learning and send them to the oracle for reannotation thus reducing the overall cost of reannotation. In this setting we tested three existing active learning methods, namely DBAL, Variance of Gradients (VoG) and BADGE. The proposed approach to data denoising is tested on the problem of offensive language detection. We observe that active learning can be effectively used for the purposes of data denoising, however care should be taken when choosing the algorithm for this purpose.

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Denoising Table-Text Retrieval for Open-Domain Question Answering
Deokhyung Kang | Baikjin Jung | Yunsu Kim | Gary Geunbae Lee

In table-text open-domain question answering, a retriever system retrieves relevant evidence from tables and text to answer questions. Previous studies in table-text open-domain question answering have two common challenges: firstly, their retrievers can be affected by false-positive labels in training datasets; secondly, they may struggle to provide appropriate evidence for questions that require reasoning across the table. To address these issues, we propose Denoised Table-Text Retriever (DoTTeR). Our approach involves utilizing a denoised training dataset with fewer false positive labels by discarding instances with lower question-relevance scores measured through a false positive detection model. Subsequently, we integrate table-level ranking information into the retriever to assist in finding evidence for questions that demand reasoning across the table. To encode this ranking information, we fine-tune a rank-aware column encoder to identify minimum and maximum values within a column. Experimental results demonstrate that DoTTeR significantly outperforms strong baselines on both retrieval recall and downstream QA tasks. Our code is available at https://github.com/deokhk/DoTTeR.

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Dependencies over Times and Tools (DoTT)
Andy Luecking | Giuseppe Abrami | Leon Hammerla | Marc Rahn | Daniel Baumartz | Steffen Eger | Alexander Mehler

Purpose: Based on the examples of English and German, we investigate to what extent parsers trained on modern variants of these languages can be transferred to older language levels without loss. Methods: We developed a treebank called DoTT (https://github.com/texttechnologylab/DoTT) which covers, roughly, the time period from 1800 until today, in conjunction with the further development of the annotation tool DependencyAnnotator. DoTT consists of a collection of diachronic corpora enriched with dependency annotations using 3 parsers, 6 pre-trained language models, 5 newly trained models for German, and two tag sets (TIGER and Universal Dependencies). To assess how the different parsers perform on texts from different time periods, we created a gold standard sample as a benchmark. Results: We found that the parsers/models perform quite well on modern texts (document-level LAS ranging from 82.89 to 88.54) and slightly worse on older texts, as expected (average document-level LAS 84.60 vs. 86.14), but not significantly. For German texts, the (German) TIGER scheme achieved slightly better results than UD. Conclusion: Overall, this result speaks for the transferability of parsers to past language levels, at least dating back until around 1800. This very transferability, it is however argued, means that studies of language change in the field of dependency syntax can draw on dependency distance but miss out on some grammatical phenomena.

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Depth Aware Hierarchical Replay Continual Learning for Knowledge Based Question Answering
Zhixiong Cao | Hai-Tao Zheng | Yangning Li | Jin Xu | Rongsheng Li | Hong-Gee Kim

Continual learning is an emerging area of machine learning that deals with the issue where models adapt well to the latest data but lose the ability to remember past data due to changes in the data source. A widely adopted solution is by keeping a small memory of previous learned data that use replay. Most of the previous studies on continual learning focused on classification tasks, such as image classification and text classification, where the model needs only to categorize the input data. Inspired by the human ability to incrementally learn knowledge and solve different problems using learned knowledge, we considered a more pratical scenario, knowledge based quesiton answering about continual learning. In this scenario, each single question is different from others(means different fact trippes to answer them) while classification tasks only need to find feature boundaries of different categories, which are the curves or surfaces that separate different categories in the feature space. To address this issue, we proposed a depth aware hierarchical replay framework which include a tree structure classfier to have a sense of knowledge distribution and fill the gap between text classfication tasks and question-answering tasks for continual learning, a local sampler to grasp these critical samples and a depth aware learning network to reconstructe the feature space of a single learning round. In our experiments, we have demonstrated that our proposed model outperforms previous continual learning methods in mitigating the issue of catastrophic forgetting.

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Depth-Wise Attention (DWAtt): A Layer Fusion Method for Data-Efficient Classification
Muhammad ElNokrashy | Badr AlKhamissi | Mona Diab

Language Models pretrained on large textual data have been shown to encode different types of knowledge simultaneously. Traditionally, only the features from the last layer are used when adapting to new tasks or data. We put forward that, when using or finetuning deep pretrained models, intermediate layer features that may be relevant to the downstream task are buried too deep to be used efficiently in terms of needed samples or steps. To test this, we propose a new layer fusion method: Depth-Wise Attention (DWAtt), to help re-surface signals from non-final layers. We compare DWAtt to a basic concatenation-based layer fusion method (Concat), and compare both to a deeper model baseline—all kept within a similar parameter budget. Our findings show that DWAtt and Concat are more step- and sample-efficient than the baseline, especially in the few-shot setting. DWAtt outperforms Concat on larger data sizes. On CoNLL-03 NER, layer fusion shows 3.68 − 9.73% F1 gain at different few-shot sizes. The layer fusion models presented significantly outperform the baseline in various training scenarios with different data sizes, architectures, and training constraints.

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Deriving Entity-Specific Embeddings from Multi-Entity Sequences
Connor Heaton | Prasenjit Mitra

Underpinning much of the recent progress in deep learning is the transformer architecture, which takes as input a sequence of embeddings E and emits an updated sequence of embeddings E’. A special [CLS] embedding is often included in this sequence, serving as a description of the sequence once processed and used as the basis for subsequent sequence-level tasks. The processed [CLS] embedding loses utility, however, when the model is presented with a multi-entity sequence and asked to perform an entity-specific task. When processing a multi-speaker dialogue, for example, the [CLS] embedding describes the entire dialogue, not any individual utterance/speaker. Existing methods toward entity-specific prediction involve redundant computation or post-processing outside of the transformer. We present a novel methodology for deriving entity-specific embeddings from a multi-entity sequence completely within the transformer, with a loose definition of entity amenable to many problem spaces. To show the generic applicability of our method, we apply it to widely different tasks: emotion recognition in conversation and player performance projection in baseball and show that it can be used to achieve SOTA in both. Code can be found at https://github.com/c-heat16/EntitySpecificEmbeddings.

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DET: A Dual-Encoding Transformer for Relational Graph Embedding
Lingbing Guo | Zhuo Chen | Jiaoyan Chen | Qiang Zhang | Huajun Chen

Despite recent successes in natural language processing and computer vision, Transformer faces scalability issues when processing graphs, e.g., computing the full node-to-node attention on knowledge graphs (KGs) with million of entities is still infeasible. The existing methods mitigate this problem by considering only the local neighbors, sacrificing the Transformer’s ability to attend to elements at any distance. This paper proposes a new Transformer architecture called Dual-Encoding Transformer (DET). DET comprises a structural encoder to aggregate information from nearby neighbors, and a semantic encoder to seek for semantically relevant nodes. We adopt a semantic neighbor search approach inspired by multiple sequence alignment (MSA) algorithms used in biological sciences. By stacking the two encoders alternately, similar to the MSA Transformer for protein representation, our method achieves superior performance compared to state-of-the-art attention-based methods on complex relational graphs like KGs and citation networks. Additionally, DET remains competitive for smaller graphs such as molecules.

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Detecting Conceptual Abstraction in LLMs
Michaela Regneri | Alhassan Abdelhalim | Soeren Laue

We show a novel approach to detecting noun abstraction within a large language model (LLM). Starting from a psychologically motivated set of noun pairs in taxonomic relationships, we instantiate surface patterns indicating hypernymy and analyze the attention matrices produced by BERT. We compare the results to two sets of counterfactuals and show that we can detect hypernymy in the abstraction mechanism, which cannot solely be related to the distributional similarity of noun pairs. Our findings are a first step towards the explainability of conceptual abstraction in LLMs.

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Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation
Sugyeong Eo | Jungwoo Lim | Chanjun Park | DaHyun Jung | Seonmin Koo | Hyeonseok Moon | Jaehyung Seo | Heuiseok Lim

Recent machine translation (MT) systems have overcome language barriers for a wide range of users, yet they still carry the risk of critical meaning deviation. Critical error detection (CED) is a task that identifies an inherent risk of catastrophic meaning distortions in the machine translation output. With the importance of reflecting cultural elements in detecting critical errors, we introduce the culture-aware “Politeness” type in detecting English-Korean critical translation errors. Besides, we facilitate two tasks by providing multiclass labels: critical error detection and critical error type classification (CETC). Empirical evaluations reveal that our introduced data augmentation approach using a newly presented perturber significantly outperforms existing baselines in both tasks. Further analysis highlights the significance of multiclass labeling by demonstrating its superior effectiveness compared to binary labels.

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Detecting Cybercrimes in Accordance with Pakistani Law: Dataset and Evaluation Using PLMs
Faizad Ullah | Ali Faheem | Ubaid Azam | Muhammad Sohaib Ayub | Faisal Kamiran | Asim Karim

Cybercrime is a serious and growing threat affecting millions of people worldwide. Detecting cybercrimes from text messages is challenging, as it requires understanding the linguistic and cultural nuances of different languages and regions. Roman Urdu is a widely used language in Pakistan and other South Asian countries, however, it lacks sufficient resources and tools for natural language processing and cybercrime detection. To address this problem, we make three main contributions in this paper. (1) We create and release CRU, a benchmark dataset for text-based cybercrime detection in Roman Urdu, which covers a number of cybercrimes as defined by the Prevention of Electronic Crimes Act (PECA) of Pakistan. This dataset is annotated by experts following a standardized procedure based on Pakistan’s legal framework. (2) We perform experiments on four pre-trained language models (PLMs) for cybercrime text classification in Roman Urdu. Our results show that xlm-roberta-base is the best model for this task, achieving the highest performance on all metrics. (3) We explore the utility of prompt engineering techniques, namely prefix and cloze prompts, for enhancing the performance of PLMs for low-resource languages such as Roman Urdu. We analyze the impact of different prompt shapes and k-shot settings on the performance of xlm-roberta-base and bert-base-multilingual-cased. We find that prefix prompts are more effective than cloze prompts for Roman Urdu classification tasks, as they provide more contextually relevant completions for the models. Our work provides useful insights and resources for future research on cybercrime detection and text classification in low-resource languages.

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Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics
Tyler A. Chang | Katrin Tomanek | Jessica Hoffmann | Nithum Thain | Erin MacMurray van Liemt | Kathleen Meier-Hellstern | Lucas Dixon

We explore a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia’s Neutral Point of View (NPOV) principle: acknowledge the absence of a single true answer and surface multiple perspectives. We frame this as retrieval augmented generation, where perspectives are retrieved from a knowledge base and the LLM is tasked with generating a fluent and faithful response from the given perspectives. As a starting point, we use a deterministic retrieval system and then focus on common LLM failure modes that arise during this approach to text generation, namely hallucination and coverage errors. We propose and evaluate three methods to detect such errors based on (1) word-overlap, (2) salience, and (3) LLM-based classifiers. Our results demonstrate that LLM-based classifiers, even when trained only on synthetic errors, achieve high error detection performance, with ROC AUC scores of 95.3% for hallucination and 90.5% for coverage error detection on unambiguous error cases. We show that when no training data is available, our other methods still yield good results on hallucination (84.0%) and coverage error (85.2%) detection.

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Detecting Impact Relevant Sections in Scientific Research
Maria Becker | Kanyao Han | Antonina Werthmann | Rezvaneh Rezapour | Haejin Lee | Jana Diesner

Impact assessment is an evolving area of research that aims at measuring and predicting the potential effects of projects or programs. Measuring the impact of scientific research is a vibrant subdomain, closely intertwined with impact assessment. A recurring obstacle pertains to the absence of an efficient framework which can facilitate the analysis of lengthy reports and text labeling. To address this issue, we propose a framework for automatically assessing the impact of scientific research projects by identifying pertinent sections in project reports that indicate the potential impacts. We leverage a mixed-method approach, combining manual annotations with supervised machine learning, to extract these passages from project reports. We experiment with different machine learning algorithms, including traditional statistical models as well as pre-trained transformer language models. Our experiments show that our proposed method achieves accuracy scores up to 0.81, and that our method is generalizable to scientific research from different domains and different languages.

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Detecting Loanwords in Emakhuwa: An Extremely Low-Resource Bantu Language Exhibiting Significant Borrowing from Portuguese
Felermino Dario Mario Ali | Henrique Lopes Cardoso | Rui Sousa-Silva

The accurate identification of loanwords within a given text holds significant potential as a valuable tool for addressing data augmentation and mitigating data sparsity issues. Such identification can improve the performance of various natural language processing tasks, particularly in the context of low-resource languages that lack standardized spelling conventions.This research proposes a supervised method to identify loanwords in Emakhuwa, borrowed from Portuguese. Our methodology encompasses a two-fold approach. Firstly, we employ traditional machine learning algorithms incorporating handcrafted features, including language-specific and similarity-based features. We build upon prior studies to extract similarity features and propose utilizing two external resources: a Sequence-to-Sequence model and a dictionary. This innovative approach allows us to identify loanwords solely by analyzing the target word without prior knowledge about its donor counterpart. Furthermore, we fine-tune the pre-trained CANINE model for the downstream task of loanword detection, which culminates in the impressive achievement of the F1-score of 93%. To the best of our knowledge, this study is the first of its kind focusing on Emakhuwa, and the preliminary results are promising as they pave the way to further advancements.

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Detecting Offensive Language in an Open Chatbot Platform
Hyeonho Song | Jisu Hong | Chani Jung | Hyojin Chin | Mingi Shin | Yubin Choi | Junghoi Choi | Meeyoung Cha

While detecting offensive language in online spaces remains an important societal issue, there is still a significant gap in existing research and practial datasets specific to chatbots. Furthermore, many of the current efforts by service providers to automatically filter offensive language are vulnerable to users’ deliberate text manipulation tactics, such as misspelling words. In this study, we analyze offensive language patterns in real logs of 6,254,261 chat utterance pairs from the commercial chat service Simsimi, which cover a variety of conversation topics. Based on the observed patterns, we introduce a novel offensive language detection method—a contrastive learning model that embeds chat content with a random masking strategy. We show that this model outperforms existing models in detecting offensive language in open-domain chat conversations while also demonstrating robustness against users’ deliberate text manipulation tactics when using offensive