Daniel Hershcovich


2024

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Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys
Yong Cao | Min Chen | Daniel Hershcovich
Findings of the Association for Computational Linguistics: EACL 2024

The cultural landscape of interactions with dialogue agents is a compelling yet relatively unexplored territory. It’s clear that various sociocultural aspects—from communication styles and beliefs to shared metaphors and knowledge—profoundly impact these interactions. To delve deeper into this dynamic, we introduce cuDialog, a first-of-its-kind benchmark for dialogue generation with a cultural lens. We also develop baseline models capable of extracting cultural attributes from dialogue exchanges, with the goal of enhancing the predictive accuracy and quality of dialogue agents. To effectively co-learn cultural understanding and multi-turn dialogue predictions, we propose to incorporate cultural dimensions with dialogue encoding features. Our experimental findings highlight that incorporating cultural value surveys boosts alignment with references and cultural markers, demonstrating its considerable influence on personalization and dialogue quality. To facilitate further exploration in this exciting domain, we publish our benchmark publicly accessible at https://github.com/yongcaoplus/cuDialog.

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Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking
Yong Cao | Ruixue Ding | Boli Chen | Xianzhi Li | Min Chen | Daniel Hershcovich | Pengjun Xie | Fei Huang
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates, which is crucial for location-related services such as navigation maps. Unlike the general sentences, Chinese geographic contexts are closely intertwined with geographical concepts, from general spans (e.g., province) to specific spans (e.g., road). Given this feature, we propose an innovative framework, namely Geo-Encoder, to more effectively integrate Chinese geographical semantics into re-ranking pipelines. Our methodology begins by employing off-the-shelf tools to associate text with geographical spans, treating them as chunking units. Then, we present a multi-task learning module to simultaneously acquire an effective attention matrix that determines chunk contributions to geographic representations. Furthermore, we put forth an asynchronous update mechanism for the proposed task, aiming to guide the model to focus on specific chunks. Experiments on two Chinese benchmark datasets, show that the Geo-Encoder achieves significant improvements when compared to state-of-the-art baselines. Notably, it leads to a substantial improvement in the Hit@1 score of MGEO-BERT, increasing it by 6.22% from 62.76 to 68.98 on the GeoTES dataset.

2023

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Pay More Attention to Relation Exploration for Knowledge Base Question Answering
Yong Cao | Xianzhi Li | Huiwen Liu | Wen Dai | Shuai Chen | Bin Wang | Min Chen | Daniel Hershcovich
Findings of the Association for Computational Linguistics: ACL 2023

Knowledge base question answering (KBQA) is a challenging task that aims to retrieve correct answers from large-scale knowledge bases. Existing attempts primarily focus on entity representation and final answer reasoning, which results in limited supervision for this task. Moreover, the relations, which empirically determine the reasoning path selection, are not fully considered in recent advancements. In this study, we propose a novel framework, RE-KBQA, that utilizes relations in the knowledge base to enhance entity representation and introduce additional supervision. We explore guidance from relations in three aspects, including (1) distinguishing similar entities by employing a variational graph auto-encoder to learn relation importance; (2) exploring extra supervision by predicting relation distributions as soft labels with a multi-task scheme; (3) designing a relation-guided re-ranking algorithm for post-processing. Experimental results on two benchmark datasets demonstrate the effectiveness and superiority of our framework, improving the F1 score by 5.8% from 40.5 to 46.3 on CWQ and 5.7% from 62.8 to 68.5 on WebQSP, better or on par with state-of-the-art methods.

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Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features
Li Zhou | Antonia Karamolegkou | Wenyu Chen | Daniel Hershcovich
Findings of the Association for Computational Linguistics: EMNLP 2023

The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD). Current understanding underscores that these tasks are substantially influenced by cultural values, however, a notable gap exists in determining if cultural features can accurately predict the success of cross-cultural transfer learning for such subjective tasks. Addressing this, our study delves into the intersection of cultural features and transfer learning effectiveness. The findings reveal that cultural value surveys indeed possess a predictive power for cross-cultural transfer learning success in OLD tasks, and that it can be further improved using offensive word distance. Based on these results, we advocate for the integration of cultural information into datasets. Additionally, we recommend leveraging data sources rich in cultural information, such as surveys, to enhance cultural adaptability. Our research signifies a step forward in the quest for more inclusive, culturally sensitive language technologies.

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A Two-Sided Discussion of Preregistration of NLP Research
Anders Søgaard | Daniel Hershcovich | Miryam de Lhoneux
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Van Miltenburg et al. (2021) suggest NLP research should adopt preregistration to prevent fishing expeditions and to promote publication of negative results. At face value, this is a very reasonable suggestion, seemingly solving many methodological problems with NLP research. We discuss pros and cons - some old, some new: a) Preregistration is challenged by the practice of retrieving hypotheses after the results are known; b) preregistration may bias NLP toward confirmatory research; c) preregistration must allow for reclassification of research as exploratory; d) preregistration may increase publication bias; e) preregistration may increase flag-planting; f) preregistration may increase p-hacking; and finally, g) preregistration may make us less risk tolerant. We cast our discussion as a dialogue, presenting both sides of the debate.

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Sentiment Classification of Historical Danish and Norwegian Literary Texts
Ali Allaith | Kirstine Degn | Alexander Conroy | Bolette Pedersen | Jens Bjerring-Hansen | Daniel Hershcovich
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

Sentiment classification is valuable for literary analysis, as sentiment is crucial in literary narratives. It can, for example, be used to investigate a hypothesis in the literary analysis of 19th-century Scandinavian novels that the writing of female authors in this period was characterized by negative sentiment, as this paper shows. In order to enable a data-driven analysis of this hypothesis, we create a manually annotated dataset of sentence-level sentiment annotations for novels from this period and use it to train and evaluate various sentiment classification methods. We find that pre-trained multilingual language models outperform models trained on modern Danish, as well as classifiers based on lexical resources. Finally, in classifier-assisted corpus analysis, we confirm the literary hypothesis regarding the author’s gender and further shed light on the temporal development of the trend. Our dataset and trained models will be useful for future analysis of historical Danish and Norwegian literary texts.

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Cross-Cultural Transfer Learning for Chinese Offensive Language Detection
Li Zhou | Laura Cabello | Yong Cao | Daniel Hershcovich
Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

Detecting offensive language is a challenging task. Generalizing across different cultures and languages becomes even more challenging: besides lexical, syntactic and semantic differences, pragmatic aspects such as cultural norms and sensitivities, which are particularly relevant in this context, vary greatly. In this paper, we target Chinese offensive language detection and aim to investigate the impact of transfer learning using offensive language detection data from different cultural backgrounds, specifically Korean and English. We find that culture-specific biases in what is considered offensive negatively impact the transferability of language models (LMs) and that LMs trained on diverse cultural data are sensitive to different features in Chinese offensive language detection. In a few-shot learning scenario, however, our study shows promising prospects for non-English offensive language detection with limited resources. Our findings highlight the importance of cross-cultural transfer learning in improving offensive language detection and promoting inclusive digital spaces.

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Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study
Yong Cao | Li Zhou | Seolhwa Lee | Laura Cabello | Min Chen | Daniel Hershcovich
Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

The recent release of ChatGPT has garnered widespread recognition for its exceptional ability to generate human-like conversations. Given its usage by users from various nations and its training on a vast multilingual corpus that includes diverse cultural and societal norms, it is crucial to evaluate its effectiveness in cultural adaptation. In this paper, we investigate the underlying cultural background of ChatGPT by analyzing its responses to questions designed to quantify human cultural differences. Our findings suggest that, when prompted with American context, ChatGPT exhibits a strong alignment with American culture, but it adapts less effectively to other cultural contexts. Furthermore, by using different prompts to probe the model, we show that English prompts reduce the variance in model responses, flattening out cultural differences and biasing them towards American culture. This study provides valuable insights into the cultural implications of ChatGPT and highlights the necessity of greater diversity and cultural awareness in language technologies.

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On Evaluating Multilingual Compositional Generalization with Translated Datasets
Zi Wang | Daniel Hershcovich
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Compositional generalization allows efficient learning and human-like inductive biases. Since most research investigating compositional generalization in NLP is done on English, important questions remain underexplored. Do the necessary compositional generalization abilities differ across languages? Can models compositionally generalize cross-lingually? As a first step to answering these questions, recent work used neural machine translation to translate datasets for evaluating compositional generalization in semantic parsing. However, we show that this entails critical semantic distortion. To address this limitation, we craft a faithful rule-based translation of the MCWQ dataset from English to Chinese and Japanese. Even with the resulting robust benchmark, which we call MCWQ-R, we show that the distribution of compositions still suffers due to linguistic divergences, and that multilingual models still struggle with cross-lingual compositional generalization. Our dataset and methodology will serve as useful resources for the study of cross-lingual compositional generalization in other tasks.

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What does the Failure to Reason with “Respectively” in Zero/Few-Shot Settings Tell Us about Language Models?
Ruixiang Cui | Seolhwa Lee | Daniel Hershcovich | Anders Søgaard
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Humans can effortlessly understand the coordinate structure of sentences such as “Niels Bohr and Kurt Cobain were born in Copenhagen and Seattle, *respectively*”. In the context of natural language inference (NLI), we examine how language models (LMs) reason with respective readings (Gawron and Kehler, 2004) from two perspectives: syntactic-semantic and commonsense-world knowledge. We propose a controlled synthetic dataset WikiResNLI and a naturally occurring dataset NatResNLI to encompass various explicit and implicit realizations of “respectively”. We show that fine-tuned NLI models struggle with understanding such readings without explicit supervision. While few-shot learning is easy in the presence of explicit cues, longer training is required when the reading is evoked implicitly, leaving models to rely on common sense inferences. Furthermore, our fine-grained analysis indicates models fail to generalize across different constructions. To conclude, we demonstrate that LMs still lag behind humans in generalizing to the long tail of linguistic constructions.

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What’s the Meaning of Superhuman Performance in Today’s NLU?
Simone Tedeschi | Johan Bos | Thierry Declerck | Jan Hajič | Daniel Hershcovich | Eduard Hovy | Alexander Koller | Simon Krek | Steven Schockaert | Rico Sennrich | Ekaterina Shutova | Roberto Navigli
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension. These PLMs have achieved impressive results on these benchmarks, even surpassing human performance in some cases. This has led to claims of superhuman capabilities and the provocative idea that certain tasks have been solved. In this position paper, we take a critical look at these claims and ask whether PLMs truly have superhuman abilities and what the current benchmarks are really evaluating. We show that these benchmarks have serious limitations affecting the comparison between humans and PLMs and provide recommendations for fairer and more transparent benchmarks.

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Probing for Hyperbole in Pre-Trained Language Models
Nina Schneidermann | Daniel Hershcovich | Bolette Pedersen
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)

Hyperbole is a common figure of speech, which is under-explored in NLP research. In this study, we conduct edge and minimal description length (MDL) probing experiments on three pre-trained language models (PLMs) in an attempt to explore the extent to which hyperbolic information is encoded in these models. We use both word-in-context and sentence-level representations as model inputs as a basis for comparison. We also annotate 63 hyperbole sentences from the HYPO dataset according to an operational taxonomy to conduct an error analysis to explore the encoding of different hyperbole categories. Our results show that hyperbole is to a limited extent encoded in PLMs, and mostly in the final layers. They also indicate that hyperbolic information may be better encoded by the sentence-level representations, which, due to the pragmatic nature of hyperbole, may therefore provide a more accurate and informative representation in PLMs. Finally, the inter-annotator agreement for our annotations, a Cohen’s Kappa of 0.339, suggest that the taxonomy categories may not be intuitive and need revision or simplification.

2022

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Compositional Generalization in Multilingual Semantic Parsing over Wikidata
Ruixiang Cui | Rahul Aralikatte | Heather Lent | Daniel Hershcovich
Transactions of the Association for Computational Linguistics, Volume 10

Semantic parsing (SP) allows humans to leverage vast knowledge resources through natural interaction. However, parsers are mostly designed for and evaluated on English resources, such as CFQ (Keysers et al., 2020), the current standard benchmark based on English data generated from grammar rules and oriented towards Freebase, an outdated knowledge base. We propose a method for creating a multilingual, parallel dataset of question-query pairs, grounded in Wikidata. We introduce such a dataset, which we call Multilingual Compositional Wikidata Questions (MCWQ), and use it to analyze the compositional generalization of semantic parsers in Hebrew, Kannada, Chinese, and English. While within- language generalization is comparable across languages, experiments on zero-shot cross- lingual transfer demonstrate that cross-lingual compositional generalization fails, even with state-of-the-art pretrained multilingual encoders. Furthermore, our methodology, dataset, and results will facilitate future research on SP in more realistic and diverse settings than has been possible with existing resources.

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Can AMR Assist Legal and Logical Reasoning?
Nikolaus Schrack | Ruixiang Cui | Hugo López | Daniel Hershcovich
Findings of the Association for Computational Linguistics: EMNLP 2022

Abstract Meaning Representation (AMR) has been shown to be useful for many downstream tasks. In this work, we explore the use of AMR for legal and logical reasoning. Specifically, we investigate if AMR can help capture logical relationships on multiple choice question answering (MCQA) tasks. We propose neural architectures that utilize linearised AMR graphs in combination with pre-trained language models. While these models are not able to outperform text-only baselines, they correctly solve different instances than the text models, suggesting complementary abilities. Error analysis further reveals that AMR parsing quality is the most prominent challenge, especially regarding inputs with multiple sentences. We conduct a theoretical analysis of how logical relations are represented in AMR and conclude it might be helpful in some logical statements but not for others.

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Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks
Ruixiang Cui | Daniel Hershcovich | Anders Søgaard
Proceedings of the First Workshop on Dynamic Adversarial Data Collection

Logical approaches to representing language have developed and evaluated computational models of quantifier words since the 19th century, but today’s NLU models still struggle to capture their semantics. We rely on Generalized Quantifier Theory for language-independent representations of the semantics of quantifier words, to quantify their contribution to the errors of NLU models. We find that quantifiers are pervasive in NLU benchmarks, and their occurrence at test time is associated with performance drops. Multilingual models also exhibit unsatisfying quantifier reasoning abilities, but not necessarily worse for non-English languages. To facilitate directly-targeted probing, we present an adversarial generalized quantifier NLI task (GQNLI) and show that pre-trained language models have a clear lack of robustness in generalized quantifier reasoning.

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A Dataset of Sustainable Diet Arguments on Twitter
Marcus Hansen | Daniel Hershcovich
Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)

Sustainable development requires a significant change in our dietary habits. Argument mining can help achieve this goal by both affecting and helping understand people’s behavior. We design an annotation scheme for argument mining from online discourse around sustainable diets, including novel evidence types specific to this domain. Using Twitter as a source, we crowdsource a dataset of 597 tweets annotated in relation to 5 topics. We benchmark a variety of NLP models on this dataset, demonstrating strong performance in some sub-tasks, while highlighting remaining challenges.

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Challenges and Strategies in Cross-Cultural NLP
Daniel Hershcovich | Stella Frank | Heather Lent | Miryam de Lhoneux | Mostafa Abdou | Stephanie Brandl | Emanuele Bugliarello | Laura Cabello Piqueras | Ilias Chalkidis | Ruixiang Cui | Constanza Fierro | Katerina Margatina | Phillip Rust | Anders Søgaard
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture. Although language and culture are tightly linked, there are important differences. Analogous to cross-lingual and multilingual NLP, cross-cultural and multicultural NLP considers these differences in order to better serve users of NLP systems. We propose a principled framework to frame these efforts, and survey existing and potential strategies.

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Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks
Ruixiang Cui | Daniel Hershcovich | Anders Søgaard
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Logical approaches to representing language have developed and evaluated computational models of quantifier words since the 19th century, but today’s NLU models still struggle to capture their semantics. We rely on Generalized Quantifier Theory for language-independent representations of the semantics of quantifier words, to quantify their contribution to the errors of NLU models. We find that quantifiers are pervasive in NLU benchmarks, and their occurrence at test time is associated with performance drops. Multilingual models also exhibit unsatisfying quantifier reasoning abilities, but not necessarily worse for non-English languages. To facilitate directly-targeted probing, we present an adversarial generalized quantifier NLI task (GQNLI) and show that pre-trained language models have a clear lack of robustness in generalized quantifier reasoning.

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Towards Climate Awareness in NLP Research
Daniel Hershcovich | Nicolas Webersinke | Mathias Kraus | Julia Bingler | Markus Leippold
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

The climate impact of AI, and NLP research in particular, has become a serious issue given the enormous amount of energy that is increasingly being used for training and running computational models. Consequently, increasing focus is placed on efficient NLP. However, this important initiative lacks simple guidelines that would allow for systematic climate reporting of NLP research. We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact, and present a quantitative survey to demonstrate this. As a remedy, we propose a climate performance model card with the primary purpose of being practically usable with only limited information about experiments and the underlying computer hardware. We describe why this step is essential to increase awareness about the environmental impact of NLP research and, thereby, paving the way for more thorough discussions.

2021

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Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color
Mostafa Abdou | Artur Kulmizev | Daniel Hershcovich | Stella Frank | Ellie Pavlick | Anders Søgaard
Proceedings of the 25th Conference on Computational Natural Language Learning

Pretrained language models have been shown to encode relational information, such as the relations between entities or concepts in knowledge-bases — (Paris, Capital, France). However, simple relations of this type can often be recovered heuristically and the extent to which models implicitly reflect topological structure that is grounded in world, such as perceptual structure, is unknown. To explore this question, we conduct a thorough case study on color. Namely, we employ a dataset of monolexemic color terms and color chips represented in CIELAB, a color space with a perceptually meaningful distance metric. Using two methods of evaluating the structural alignment of colors in this space with text-derived color term representations, we find significant correspondence. Analyzing the differences in alignment across the color spectrum, we find that warmer colors are, on average, better aligned to the perceptual color space than cooler ones, suggesting an intriguing connection to findings from recent work on efficient communication in color naming. Further analysis suggests that differences in alignment are, in part, mediated by collocationality and differences in syntactic usage, posing questions as to the relationship between color perception and usage and context.

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A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs
Mareike Hartmann | Miryam de Lhoneux | Daniel Hershcovich | Yova Kementchedjhieva | Lukas Nielsen | Chen Qiu | Anders Søgaard
Proceedings of the 25th Conference on Computational Natural Language Learning

Negation is one of the most fundamental concepts in human cognition and language, and several natural language inference (NLI) probes have been designed to investigate pretrained language models’ ability to detect and reason with negation. However, the existing probing datasets are limited to English only, and do not enable controlled probing of performance in the absence or presence of negation. In response, we present a multilingual (English, Bulgarian, German, French and Chinese) benchmark collection of NLI examples that are grammatical and correctly labeled, as a result of manual inspection and reformulation. We use the benchmark to probe the negation-awareness of multilingual language models and find that models that correctly predict examples with negation cues, often fail to correctly predict their counter-examples without negation cues, even when the cues are irrelevant for semantic inference.

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Lexical Semantic Recognition
Nelson F. Liu | Daniel Hershcovich | Michael Kranzlein | Nathan Schneider
Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)

In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation. As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.

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Great Service! Fine-grained Parsing of Implicit Arguments
Ruixiang Cui | Daniel Hershcovich
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)

Broad-coverage meaning representations in NLP mostly focus on explicitly expressed content. More importantly, the scarcity of datasets annotating diverse implicit roles limits empirical studies into their linguistic nuances. For example, in the web review “Great service!”, the provider and consumer are implicit arguments of different types. We examine an annotated corpus of fine-grained implicit arguments (Cui and Hershcovich, 2020) by carefully re-annotating it, resolving several inconsistencies. Subsequently, we present the first transition-based neural parser that can handle implicit arguments dynamically, and experiment with two different transition systems on the improved dataset. We find that certain types of implicit arguments are more difficult to parse than others and that the simpler system is more accurate in recovering implicit arguments, despite having a lower overall parsing score, attesting current reasoning limitations of NLP models. This work will facilitate a better understanding of implicit and underspecified language, by incorporating it holistically into meaning representations.

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Moses and the Character-Based Random Babbling Baseline: CoAStaL at AmericasNLP 2021 Shared Task
Marcel Bollmann | Rahul Aralikatte | Héctor Murrieta Bello | Daniel Hershcovich | Miryam de Lhoneux | Anders Søgaard
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

We evaluated a range of neural machine translation techniques developed specifically for low-resource scenarios. Unsuccessfully. In the end, we submitted two runs: (i) a standard phrase-based model, and (ii) a random babbling baseline using character trigrams. We found that it was surprisingly hard to beat (i), in spite of this model being, in theory, a bad fit for polysynthetic languages; and more interestingly, that (ii) was better than several of the submitted systems, highlighting how difficult low-resource machine translation for polysynthetic languages is.

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How far can we get with one GPU in 100 hours? CoAStaL at MultiIndicMT Shared Task
Rahul Aralikatte | Héctor Ricardo Murrieta Bello | Miryam de Lhoneux | Daniel Hershcovich | Marcel Bollmann | Anders Søgaard
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This work shows that competitive translation results can be obtained in a constrained setting by incorporating the latest advances in memory and compute optimization. We train and evaluate large multilingual translation models using a single GPU for a maximum of 100 hours and get within 4-5 BLEU points of the top submission on the leaderboard. We also benchmark standard baselines on the PMI corpus and re-discover well-known shortcomings of translation systems and metrics.

2020

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Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing
Stephan Oepen | Omri Abend | Lasha Abzianidze | Johan Bos | Jan Hajič | Daniel Hershcovich | Bin Li | Tim O'Gorman | Nianwen Xue | Daniel Zeman
Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing

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MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing
Stephan Oepen | Omri Abend | Lasha Abzianidze | Johan Bos | Jan Hajic | Daniel Hershcovich | Bin Li | Tim O’Gorman | Nianwen Xue | Daniel Zeman
Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu

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HUJI-KU at MRP 2020: Two Transition-based Neural Parsers
Ofir Arviv | Ruixiang Cui | Daniel Hershcovich
Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing

This paper describes the HUJI-KU system submission to the shared task on CrossFramework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task. Both are transition-based parsers using BERT contextualized embeddings. We generalized TUPA to support the newly-added MRP frameworks and languages, and experimented with multitask learning with the HIT-SCIR parser. We reached 4th place in both the crossframework and cross-lingual tracks.

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Refining Implicit Argument Annotation for UCCA
Ruixiang Cui | Daniel Hershcovich
Proceedings of the Second International Workshop on Designing Meaning Representations

Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation’s foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes.

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Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
Daniel Hershcovich | Miryam de Lhoneux | Artur Kulmizev | Elham Pejhan | Joakim Nivre
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

We present Køpsala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020. Our system is a pipeline consisting of off-the-shelf models for everything but enhanced graph parsing, and for the latter, a transition-based graph parser adapted from Che et al. (2019). We train a single enhanced parser model per language, using gold sentence splitting and tokenization for training, and rely only on tokenized surface forms and multilingual BERT for encoding. While a bug introduced just before submission resulted in a severe drop in precision, its post-submission fix would bring us to 4th place in the official ranking, according to average ELAS. Our parser demonstrates that a unified pipeline is effective for both Meaning Representation Parsing and Enhanced Universal Dependencies.

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Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics
Daniel Hershcovich | Nathan Schneider | Dotan Dvir | Jakob Prange | Miryam de Lhoneux | Omri Abend
Proceedings of the 28th International Conference on Computational Linguistics

Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other. To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods: (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features. We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation). Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser quality—indicating that UCCA annotations are partially redundant with STREUSLE annotations. Despite this substantial convergence between frameworks, we find several important areas of divergence.

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Cross-lingual Semantic Representation for NLP with UCCA
Omri Abend | Dotan Dvir | Daniel Hershcovich | Jakob Prange | Nathan Schneider
Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts

This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew. UCCA builds on extensive typological work and supports rapid annotation. The tutorial will provide a detailed introduction to the UCCA annotation guidelines, design philosophy and the available resources; and a comparison to other meaning representations. It will also survey the existing parsing work, including the findings of three recent shared tasks, in SemEval and CoNLL, that addressed UCCA parsing. Finally, the tutorial will present recent applications and extensions to the scheme, demonstrating its value for natural language processing in a range of languages and domains.

2019

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Argument Invention from First Principles
Yonatan Bilu | Ariel Gera | Daniel Hershcovich | Benjamin Sznajder | Dan Lahav | Guy Moshkowich | Anael Malet | Assaf Gavron | Noam Slonim
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Competitive debaters often find themselves facing a challenging task – how to debate a topic they know very little about, with only minutes to prepare, and without access to books or the Internet? What they often do is rely on ”first principles”, commonplace arguments which are relevant to many topics, and which they have refined in past debates. In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic. As far as we know, this is the first time that this approach to argument invention is formalized and made explicit in the context of NLP. The main goal of this work is to show that it is possible to define such a taxonomy. While the taxonomy suggested here should be thought of as a ”first attempt” it is nonetheless coherent, covers well the relevant topics and coincides with what professional debaters actually argue in their speeches, and facilitates automatic argument invention for new topics.

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The Language of Legal and Illegal Activity on the Darknet
Leshem Choshen | Dan Eldad | Daniel Hershcovich | Elior Sulem | Omri Abend
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity. Given the magnitude of these networks, scalably monitoring their activity necessarily relies on automated tools, and notably on NLP tools. However, little is known about what characteristics texts communicated through the Darknet have, and how well do off-the-shelf NLP tools do on this domain. This paper tackles this gap and performs an in-depth investigation of the characteristics of legal and illegal text in the Darknet, comparing it to a clear net website with similar content as a control condition. Taking drugs-related websites as a test case, we find that texts for selling legal and illegal drugs have several linguistic characteristics that distinguish them from one another, as well as from the control condition, among them the distribution of POS tags, and the coverage of their named entities in Wikipedia.

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SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA
Daniel Hershcovich | Zohar Aizenbud | Leshem Choshen | Elior Sulem | Ari Rappoport | Omri Abend
Proceedings of the 13th International Workshop on Semantic Evaluation

We present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. The shared task has yielded improvements over the state-of-the-art baseline in all languages and settings. Full results can be found in the task’s website https://competitions.codalab.org/competitions/19160.

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Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning
Stephan Oepen | Omri Abend | Jan Hajic | Daniel Hershcovich | Marco Kuhlmann | Tim O’Gorman | Nianwen Xue
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

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MRP 2019: Cross-Framework Meaning Representation Parsing
Stephan Oepen | Omri Abend | Jan Hajic | Daniel Hershcovich | Marco Kuhlmann | Tim O’Gorman | Nianwen Xue | Jayeol Chun | Milan Straka | Zdenka Uresova
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five distinct approaches to the representation of sentence meaning in the form of directed graph were represented in the training and evaluation data for the task, packaged in a uniform abstract graph representation and serialization. The task received submissions from eighteen teams, of which five do not participate in the official ranking because they arrived after the closing deadline, made use of additional training data, or involved one of the task co-organizers. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu

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TUPA at MRP 2019: A Multi-Task Baseline System
Daniel Hershcovich | Ofir Arviv
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

This paper describes the TUPA system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). Because it was prepared by one of the task co-organizers, TUPA provides a baseline point of comparison and is not considered in the official ranking of participating systems. While originally developed for UCCA only, TUPA has been generalized to support all MRP frameworks included in the task, and trained using multi-task learning to parse them all with a shared model. It is a transition-based parser with a BiLSTM encoder, augmented with BERT contextualized embeddings.

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Content Differences in Syntactic and Semantic Representation
Daniel Hershcovich | Omri Abend | Ari Rappoport
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which hinders the development of parsing methods informed by the details of target schemes and constructions. We target this gap, and take Universal Dependencies (UD) and UCCA as a test case. After abstracting away from differences of convention or formalism, we find that most content divergences can be ascribed to: (1) UCCA’s distinction between a Scene and a non-Scene; (2) UCCA’s distinction between primary relations, secondary ones and participants; (3) different treatment of multi-word expressions, and (4) different treatment of inter-clause linkage. We further discuss the long tail of cases where the two schemes take markedly different approaches. Finally, we show that the proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. The substantial differences between the schemes suggest that semantic parsers are likely to benefit downstream text understanding applications beyond their syntactic counterparts.

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Rewarding Coreference Resolvers for Being Consistent with World Knowledge
Rahul Aralikatte | Heather Lent | Ana Valeria Gonzalez | Daniel Hershcovich | Chen Qiu | Anders Sandholm | Michael Ringaard | Anders Søgaard
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples. We show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases. Since relation extraction systems can rely on different forms of supervision and be biased in different ways, we obtain the best performance, improving over the state of the art, using multi-task reinforcement learning.

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Syntactic Interchangeability in Word Embedding Models
Daniel Hershcovich | Assaf Toledo | Alon Halfon | Noam Slonim
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP

Nearest neighbors in word embedding models are commonly observed to be semantically similar, but the relations between them can vary greatly. We investigate the extent to which word embedding models preserve syntactic interchangeability, as reflected by distances between word vectors, and the effect of hyper-parameters—context window size in particular. We use part of speech (POS) as a proxy for syntactic interchangeability, as generally speaking, words with the same POS are syntactically valid in the same contexts. We also investigate the relationship between interchangeability and similarity as judged by commonly-used word similarity benchmarks, and correlate the result with the performance of word embedding models on these benchmarks. Our results will inform future research and applications in the selection of word embedding model, suggesting a principle for an appropriate selection of the context window size parameter depending on the use-case.

2018

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Universal Dependency Parsing with a General Transition-Based DAG Parser
Daniel Hershcovich | Omri Abend | Ari Rappoport
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task. TUPA is a general neural transition-based DAG parser, which we use to present the first experiments on recovering enhanced dependencies as part of the general parsing task. TUPA was designed for parsing UCCA, a cross-linguistic semantic annotation scheme, exhibiting reentrancy, discontinuity and non-terminal nodes. By converting UD trees and graphs to a UCCA-like DAG format, we train TUPA almost without modification on the UD parsing task. The generic nature of our approach lends itself naturally to multitask learning.

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Multitask Parsing Across Semantic Representations
Daniel Hershcovich | Omri Abend | Ari Rappoport
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others. In this paper we tackle the challenging task of improving semantic parsing performance, taking UCCA parsing as a test case, and AMR, SDP and Universal Dependencies (UD) parsing as auxiliary tasks. We experiment on three languages, using a uniform transition-based system and learning architecture for all parsing tasks. Despite notable conceptual, formal and domain differences, we show that multitask learning significantly improves UCCA parsing in both in-domain and out-of-domain settings.

2017

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A Transition-Based Directed Acyclic Graph Parser for UCCA
Daniel Hershcovich | Omri Abend | Ari Rappoport
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.

2015

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Automatic Claim Negation: Why, How and When
Yonatan Bilu | Daniel Hershcovich | Noam Slonim
Proceedings of the 2nd Workshop on Argumentation Mining

2014

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A Benchmark Dataset for Automatic Detection of Claims and Evidence in the Context of Controversial Topics
Ehud Aharoni | Anatoly Polnarov | Tamar Lavee | Daniel Hershcovich | Ran Levy | Ruty Rinott | Dan Gutfreund | Noam Slonim
Proceedings of the First Workshop on Argumentation Mining

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Context Dependent Claim Detection
Ran Levy | Yonatan Bilu | Daniel Hershcovich | Ehud Aharoni | Noam Slonim
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Claims on demand – an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora
Noam Slonim | Ehud Aharoni | Carlos Alzate | Roy Bar-Haim | Yonatan Bilu | Lena Dankin | Iris Eiron | Daniel Hershcovich | Shay Hummel | Mitesh Khapra | Tamar Lavee | Ran Levy | Paul Matchen | Anatoly Polnarov | Vikas Raykar | Ruty Rinott | Amrita Saha | Naama Zwerdling | David Konopnicki | Dan Gutfreund
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations

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