Previous work on the automatic identification of fallacies in natural language text has typically approached the problem in constrained experimental setups that make it difficult to understand the applicability and usefulness of the proposals in the real world. In this paper, we present the first analysis of the limitations that these data-driven approaches could show in real situations. For that purpose, we first create a validation corpus consisting of natural language argumentation schemes. Second, we provide new empirical results to the emerging task of identifying fallacies in natural language text. Third, we analyse the errors observed outside of the testing data domains considering the new validation corpus. Finally, we point out some important limitations observed in our analysis that should be taken into account in future research in this topic. Specifically, if we want to deploy these systems in the Wild.

This paper introduces an approach which operationalizes the role of discourse connectives for detecting argument stance. Specifically, the study investigates the utility of masked language model probabilities of discourse connectives inserted between a claim and a premise that supports or attacks it. The research focuses on a range of connectives known to signal support or attack, such as because, but, so, or although. By employing a LightGBM classifier, the study reveals promising results in stance detection in English discourse. While the proposed system does not aim to outperform state-of-the-art architectures, the classification accuracy is surprisingly high, highlighting the potential of these features to enhance argument mining tasks, including stance detection.

The use of argumentation in education has shown improvement in students’ critical thinking skills, and computational models for argumentation have been developed to further assist this process. Although these models are useful for evaluating the quality of an argument, they often cannot explain why a particular argument score was predicted, i.e., why the argument is good or bad, which makes it difficult to provide constructive feedback to users, e.g., students, so that they can strengthen their critical thinking skills. In this survey, we explore current NLP feedback systems by categorizing each into four important dimensions of feedback (Richness, Visualization, Interactivity and Personalization). We discuss limitations for each dimension and provide suggestions to enhance the power of feedback and explanations to ultimately improve user critical thinking skills.

The conventional method of extracting arguments from sentences solely relies on word proximity, disregarding the syntactic structure of the sentence. This approach often leads to inaccuracies, especially when identifying argumentative span boundaries. In this research, we investigate the benefits of utilizing a constituency tree representation of sentences to predict Argument Discourse Units (ADUs) at the token level. We first evaluate the effectiveness of utilizing the constituency tree representation for capturing the structural attributes of arguments within sentences. We demonstrate empirically that the constituency structure surpasses simple linear dependencies among neighboring words in terms of effectiveness. Our approach involves leveraging graph neural networks in conjunction with the constituency tree, adapting it specifically for argument unit recognition. Through extensive evaluation, our model outperforms existing approaches in recognizing argument units at the token level. Furthermore, we employ explainability methods to assess the suitability of our model architecture, providing insights into its performance.

We propose a re-ranking approach to improve the retrieval effectiveness for non-factual comparative queries like ‘Which city is better, London or Paris?’ based on whether the results express a stance towards the comparison objects (London vs. Paris) or not. Applied to the 26 runs submitted to the Touché 2022 task on comparative argument retrieval, our stance-aware re-ranking significantly improves the retrieval effectiveness for all runs when perfect oracle-style stance labels are available. With our most effective practical stance detector based on GPT-3.5 (F₁ of 0.49 on four stance classes), our re-ranking still improves the effectiveness for all runs but only six improvements are significant. Artificially “deteriorating” the oracle-style labels, we further find that an F₁ of 0.90 for stance detection is necessary to significantly improve the retrieval effectiveness for the best run via stance-aware re-ranking.

Legal arguments are one of the key aspects of legal knowledge which are expressed in various ways in the unstructured text of court judgements. A large database of past legal arguments can be created by extracting arguments from court judgements, categorizing them, and storing them in a structured format. Such a database would be useful for suggesting suitable arguments for any new case. In this paper, we focus on extracting arguments from Indian Supreme Court judgements using minimal supervision. We first identify a set of certain sentence-level argument markers which are useful for argument extraction such as whether a sentence contains a claim or not, whether a sentence is argumentative in nature, whether two sentences are part of the same argument, etc. We then model the legal argument extraction problem as a text segmentation problem where we combine multiple weak evidences in the form of argument markers using Integer Linear Programming (ILP), finally arriving at a global document-level solution giving the most optimal legal arguments. We demonstrate the effectiveness of our technique by comparing it against several competent baselines.

Learning to make effective arguments is vital for the development of critical-thinking in students and, hence, for their academic and career success. Detecting argument components is crucial for developing systems that assess students’ ability to develop arguments. Traditionally, supervised learning has been used for this task, but this requires a large corpus of reliable training examples which are often impractical to obtain for student writing. Large language models have also been shown to be effective few-shot learners, making them suitable for low-resource argument detection. However, concerns such as latency, service reliability, and data privacy might hinder their practical applicability. To address these challenges, we present a low-resource classification approach that combines the intrinsic entailment relationship among the argument elements with a parameter-efficient prompt-tuning strategy. Experimental results demonstrate the effectiveness of our method in reducing the data and computation requirements of training an argument detection model without compromising the prediction accuracy. This suggests the practical applicability of our model across a variety of real-world settings, facilitating broader access to argument classification for researchers spanning various domains and problem scenarios.

We define an argumentation strategy as the set of rhetorical and stylistic means that authors employ to produce an effective, and often persuasive, text. First computational accounts of such strategies have been relatively coarse-grained, while in our work we aim to move to a more detailed analysis. We extend the annotations of the Argument Annotated Essays corpus (Stab and Gurevych, 2017) with specific types of claims and premises, propose a model for their automatic identification and show first results, and then we discuss usage patterns that emerge with respect to the essay structure, the “flows” of argument component types, the claim-premise constellations, the role of the essay prompt type, and that of the individual author.

Recent approaches to argument mining have focused on training machine learning algorithms from annotated text corpora, utilizing as input high-dimensional linguistic feature vectors. Differently to previous work, in this paper, we preliminarily investigate the potential benefits of reducing the dimensionality of the input data. Through an empirical study, testing SVD, PCA and LDA techniques on a new argumentative corpus in Spanish for an underexplored domain (e-participation), and using a novel, rich argument model, we show positive results in terms of both computation efficiency and argumentative information extraction effectiveness, for the three major argument mining tasks: argumentative fragment detection, argument component classification, and argumentative relation recognition. On a space with dimension around 3-4% of the number of input features, the argument mining methods are able to reach 95-97% of the performance achieved by using the entire corpus, and even surpass it in some cases.

Debate naturalness ranges on a scale from small, highly structured, and topically focused settings to larger, more spontaneous and less constrained environments. The more unconstrained a debate, the more spontaneous speakers act: they build on contextual knowledge and use anaphora or ellipses to construct their arguments. They also use rhetorical devices such as questions and imperatives to support or attack claims. In this paper, we study how the reconstruction of the actual debate contributions, i.e., utterances which contain pronouns, ellipses and fuzzy language, into full-fledged propositions which are interpretable without context impacts the prediction of argument relations and investigate the effect of incorporating contextual information for the task. We work with highly complex spontaneous debates with more than 10 speakers on a wide variety of topics. We find that in contrast to our initial hypothesis, reconstruction does not improve predictions and context only improves them when used in combination with propositions.

Framing is an important mechanism in argumentation, as participants in a debate tend to emphasize those aspects or dimensions of the issue under debate that support their standpoint. The task of reframing an argument, that is changing the underlying framing, has received increasing attention recently. We propose a novel unsupervised approach to argument reframing that takes inspiration from counterfactual explanation generation approaches in the field of eXplainable AI (XAI). We formalize the task as a mask-and-replace approach in which an LLM is tasked to replace masked tokens associated with a set of frames to be eliminated by other tokens related to a set of target frames to be added. Our method relies on two key mechanisms: framed decoding and reranking based on a number of metrics similar to those used in XAI to search for a suitable counterfactual. We evaluate our approach on three topics using the dataset by Ruckdeschel and Wiedemann (2022). We show that our two key mechanisms outperform an unguided LLM as a baseline by increasing the ratio of successfully reframed arguments by almost an order of magnitude.

This paper presents an overview of the ImageArg shared task, the first multimodal Argument Mining shared task co-located with the 10th Workshop on Argument Mining at EMNLP 2023. The shared task comprises two classification subtasks - (1) Subtask-A: Argument Stance Classification; (2) Subtask-B: Image Persuasiveness Classification. The former determines the stance of a tweet containing an image and a piece of text toward a controversial topic (e.g., gun control and abortion). The latter determines whether the image makes the tweet text more persuasive. The shared task received 31 submissions for Subtask-A and 21 submissions for Subtask-B from 9 different teams across 6 countries. The top submission in Subtask-A achieved an F1-score of 0.8647 while the best submission in Subtask-B achieved an F1-score of 0.5561.

ImageArg is a shared task at the 10th ArgMining Workshop at EMNLP 2023. It leverages the ImageArg dataset to advance multimodal persuasiveness techniques. This challenge comprises two distinct subtasks: 1) Argumentative Stance (AS) Classification: Assessing whether a given tweet adopts an argumentative stance. 2) Image Persuasiveness (IP) Classification: Determining if the tweet image enhances the persuasive quality of the tweet. We conducted various experiments on both subtasks and ranked sixth out of the nine participating teams.

A main goal of Argument Mining (AM) is to analyze an author’s stance. Unlike previous AM datasets focusing only on text, the shared task at the 10th Workshop on Argument Mining introduces a dataset including both texts and images. Importantly, these images contain both visual elements and optical characters. Our new framework, TILFA (A Unified Framework for Text, Image, and Layout Fusion in Argument Mining), is designed to handle this mixed data. It excels at not only understanding text but also detecting optical characters and recognizing layout details in images. Our model significantly outperforms existing baselines, earning our team, KnowComp, the 1st place in the leaderboard of Argumentative Stance Classification subtask in this shared task.

An important property of argumentation concerns the degree of its persuasiveness, which can be influenced by various modalities. On social media platforms, individuals usually have the option of supporting their textual statements with images. The goals of the ImageArg shared task, held with ArgMining 2023, were therefore (A) to classify tweet stances considering both modalities and (B) to predict the influence of an image on the persuasiveness of a tweet text. In this paper, we present our proposed methodology that shows strong performance on both tasks, placing 3rd team on the leaderboard in each case with F1 scores of 0.8273 (A) and 0.5281 (B). The framework relies on pre-trained models to extract text and image features, which are then fed into a task-specific classification model. Our experiments highlighted that the multimodal vision and language model CLIP holds a specific importance in the extraction of features, in particular for task (A).

This paper reports on the submissions of Webis to the two subtasks of ImageArg 2023. For the subtask of argumentative stance classification, we reached an F1 score of 0.84 using a BERT model for sequence classification. For the subtask of image persuasiveness classification, we reached an F1 score of 0.56 using CLIP embeddings and a neural network model, achieving the best performance for this subtask in the competition. Our analysis reveals that seemingly clear sentences (e.g., “I support gun control”) are still problematic for our otherwise competitive stance classifier and that ignoring the tweet text for image persuasiveness prediction leads to a model that is similarly effective to our top-performing model.

With the rising prominence of social media, users frequently supplement their written content with images. This trend has brought about new challenges in automatic processing of social media messages. In order to fully understand the meaning of a post, it is necessary to capture the relationship between the image and the text. In this work we address the two main objectives of the ImageArg shared task. Firstly, we aim to determine the stance of a multi-modal tweet toward a particular issue. We propose a strong baseline, fine-tuning transformer based models on concatenation of tweet text and image text. The second goal is to predict the impact of an image on the persuasiveness of the text in a multi-modal tweet. To capture the persuasiveness of an image, we train vision and language models on the data and explore other sets of features merged with the model, to enhance prediction power. Ultimately, both of these goals contribute toward the broader aim of understanding multi-modal messages on social media and how images and texts relate to each other.

To advance argumentative stance prediction as a multimodal problem, the First Shared Task in Multimodal Argument Mining hosted stance prediction in crucial social topics of gun control and abortion. Our exploratory study attempts to evaluate the necessity of images for stance prediction in tweets and compare out-of-the-box text-based large-language models (LLM) in few-shot settings against fine-tuned unimodal and multimodal models. Our work suggests an ensemble of fine-tuned text-based language models (0.817 F1-score) outperforms both the multimodal (0.677 F1-score) and text-based few-shot prediction using a recent state-of-the-art LLM (0.550 F1-score). In addition to the differences in performance, our findings suggest that the multimodal models tend to perform better when image content is summarized as natural language over their native pixel structure and, using in-context examples improves few-shot learning of LLMs performance.

Persuasiveness is a prominent personality trait that measures the extent to which a speaker can impact the beliefs, attitudes, intentions, motivations, and actions of their audience. The ImageArg task is a featured challenge at the 10th ArgMining Workshop during EMNLP 2023, focusing on harnessing the potential of the ImageArg dataset to advance techniques in multimodal persuasion. In this study, we investigate the utilization of dual-modality datasets and evaluate three distinct multi-modality models. By enhancing multi-modality datasets, we demonstrate both the advantages and constraints of cutting-edge models.

In this paper, we describe our system for ImageArg-2023 Shared Task that aims to identify an image’s stance towards a tweet and determine its persuasiveness score concerning a specific topic. In particular, the Shared Task proposes two subtasks viz. subtask (A) Multimodal Argument Stance (AS) Classification, and subtask (B) Multimodal Image Persuasiveness (IP) Classification, using a dataset composed of tweets (images and text) from controversial topics, namely gun control and abortion. For subtask A, we employ multiple transformer models using a text based approach to classify the argumentative stance of the tweet. For sub task B we adopted text based as well as multimodal learning methods to classify image persuasiveness of the tweet. Surprisingly, the text-based approach of the tweet overall performed better than the multimodal approaches considered. In summary, our best system achieved a F1 score of 0.85 for sub task (A) and 0.50 for subtask (B), and ranked 2nd in subtask (A) and 4th in subtask (B), among all teams submissions.

Peer review is the key quality control mechanism in science. The core component of peer review are the review reports – argumentative texts where the reviewers evaluate the work and make suggestions to the authors. Reviewing is a demanding expert task prone to bias. An active line of research in NLP aims to support peer review via automatic analysis of review reports. This research meets two key challenges. First, NLP to date has focused on peer reviews from machine learning conferences. Yet, NLP models are prone to domain shift and might underperform when applied to reviews from a new research community. Second, while some venues make their reviewing processes public, peer reviewing data is generally hard to obtain and expensive to label. Approaches to low-data NLP processing for peer review remain under-investigated. Enabled by the recent release of open multi-domain corpora of peer reviews, the PragTag-2023 Shared Task explored the ways to increase domain robustness and address data scarcity in pragmatic tagging – a sentence tagging task where review statements are classified by their argumentative function. This paper describes the shared task, outlines the participating systems, and summarizes the results.

This paper describes our contribution to the PragTag-2023 Shared Task. We describe and compare different approaches based on sentence classification, sentence similarity, and sequence tagging. We find that a BERT-based sentence labeling approach integrating positional information outperforms both sequence tagging and SBERT-based sentence classification. We further provide analyses highlighting the potential of combining different approaches.

Due to the scarcity of review data and the high annotation cost, in this paper, we primarily delve into the fine-tuning of pretrained models using limited data. To enhance the robustness of the model, we employ adversarial training techniques. By introducing subtle perturbations, we compel the model to better cope with adversarial attacks, thereby increasing the stability of the model in input data. We utilize pooling techniques to aid the model in extracting critical information, reducing computational complexity, and improving the model’s generalization capability. Experimental results demonstrate the effectiveness of our proposed approach on a review paper dataset with limited data volume.

This paper describes our submission to the PragTag task, which aims to categorize each sentence from peer reviews into one of the six distinct pragmatic tags. The task consists of three conditions: full, low, and zero, each distinguished by the number of training data and further categorized into five distinct domains. The main challenge of this task is the domain shift, which is exacerbated by non-uniform distribution and the limited availability of data across the six pragmatic tags and their respective domains. To address this issue, we predominantly employ two data augmentation techniques designed to mitigate data imbalance and scarcity: pseudo-labeling and synonym generation. We experimentally demonstrate the effectiveness of our approaches, achieving the first rank under the zero condition and the third in the full and low conditions.

We describe our models for the Pragmatic Tagging of Peer Reviews Shared Task at the 10th Workshop on Argument Mining at EMNLP-2023. We trained multiple sentence classification models for the above competition task by employing various state-of-the-art transformer models that can be fine-tuned either in the traditional way or through instruction-based fine-tuning. Multiple model predictions on unlabeled data are combined to tentatively label unlabeled instances and augment the dataset to further improve performance on the prediction task. In particular, on the F1000RD corpus, we perform on-par with models trained on 100% of the training data while using only 10% of the data. Overall, on the competition datasets, we rank among the top-2 performers for the different data conditions.

Most transformer models are trained on English language corpus that contain text from forums like Wikipedia and Reddit. While these models are being used in many specialized domains such as scientific peer review, legal, and healthcare, their performance is subpar because they do not contain the information present in data relevant to such specialized domains. To help these models perform as well as possible on specialized domains, one of the approaches is to collect labeled data of that particular domain and fine-tune the transformer model of choice on such data. While a good approach, it suffers from the challenge of collecting a lot of labeled data which requires significant manual effort. Another way is to use unlabeled domain-specific data to pre-train these transformer model and then fine-tune this model on labeled data. We evaluate how transformer models perform when fine-tuned on labeled data after initial pre-training with unlabeled data. We compare their performance with a transformer model fine-tuned on labeled data without initial pre-training with unlabeled data. We perform this comparison on a dataset of Scientific Peer Reviews provided by organizers of PragTag-2023 Shared Task and observe that a transformer model fine-tuned on labeled data after initial pre-training on unlabeled data using Masked Language Modelling outperforms a transformer model fine-tuned only on labeled data without initial pre-training with unlabeled data using Masked Language Modelling.

Extending semantic parsers to code-switched input has been a challenging problem, primarily due to a lack of supervised training data. In this work, we introduce CST5, a new data augmentation technique that fine-tunes a T5 model using a small seed set (≈100 utterances) to generate code-switched utterances from English utterances. We show that CST5 generates high quality code-switched data, both intrinsically (per human evaluation) and extrinsically by comparing baseline models which are trained without data augmentation to models which are trained with augmented data. Empirically we observe that using CST5, one can achieve the same semantic parsing performance by using up to 20x less labeled data. To aid further research in this area, we are also releasing (a) Hinglish-TOP, the largest human annotated code-switched semantic parsing dataset to date, containing 10k human annotated Hindi-English (Hinglish) code-switched utterances, and (b) Over 170K CST5 generated code-switched utterances from the TOPv2 dataset. Human evaluation shows that both the human annotated data as well as the CST5 generated data is of good quality.

We present PandaGPT, an approach to emPower large lANguage moDels with visual and Auditory instruction-following capabilities. Our pilot experiments show that PandaGPT can perform complex tasks such as detailed image description generation, writing stories inspired by videos, and answering questions about audios. More interestingly, PandaGPT can take multimodal inputs simultaneously and compose their semantics naturally. For example, PandaGPT can connect how objects look in an image/video and how they sound in an audio. To do so, PandaGPT combines the multimodal encoders from ImageBind and the large language models from Vicuna. Notably, only aligned image-text pairs are required for the training of PandaGPT. Thanks to the strong capability of ImageBind in embedding data from different modalities into the same space, PandaGPT displays emergent, i.e. zero-shot, cross-modal behaviors for data other than image and text (e.g., video, audio, depth, thermal, and IMU). We hope that PandaGPT serves as an initial step toward building AGI that can perceive and understand inputs in different modalities holistically, as we humans do.

Conditional natural language generation methods often require either expensive fine-tuning or training a large language model from scratch. Both are unlikely to lead to good results without a substantial amount of data and computational resources. Prompt learning without changing the parameters of a large language model presents a promising alternative. It is a cost-effective approach, while still achieving competitive results. While this procedure is now established for zero- and few-shot text classification and structured prediction, it has received limited attention in conditional text generation. We present the first automatic prompt optimization approach for emotion-conditioned text generation with instruction-fine-tuned models. Our method uses an iterative optimization procedure that changes the prompt by adding, removing, or replacing tokens. As objective function, we only require a text classifier that measures the realization of the conditional variable in the generated text. We evaluate the method on emotion-conditioned text generation with a focus on event reports and compare it to manually designed prompts that also act as the seed for the optimization procedure. The optimized prompts achieve 0.75 macro-average F1 to fulfill the emotion condition in contrast to manually designed seed prompts with only 0.22 macro-average F1.

LLMs are known to be very powerful, exhibiting both great benefits and great risk. We seek to leverage the benefits, in particular the ability to be fluent, conversational dialogue agents, while minimizing the risks, such as hallucination and toxic content. In this work we use knowledge distillation to create a virtual museum tour guide dialogue agent, employing ChatGPT as a teacher model for a smaller student model, T5-large. We find the T5 model shows competitive performance, significantly reduces instances of hallucination, and shows promise for reducing toxic content.

In this paper, we investigate the use of large language models (LLMs) like ChatGPT for document-grounded response generation in the context of information-seeking dialogues. For evaluation, we use the MultiDoc2Dial corpus of task-oriented dialogues in four social service domains previously used in the DialDoc 2022 Shared Task. Information-seeking dialogue turns are grounded in multiple documents providing relevant information. We generate dialogue completion responses by prompting a ChatGPT model, using two methods: Chat-Completion and LlamaIndex. ChatCompletion uses knowledge from ChatGPT model pre-training while LlamaIndex also extracts relevant information from documents. Observing that document-grounded response generation via LLMs cannot be adequately assessed by automatic evaluation metrics as they are significantly more verbose, we perform a human evaluation where annotators rate the output of the shared task winning system, the two ChatGPT variants outputs, and human responses. While both ChatGPT variants are more likely to include information not present in the relevant segments, possibly including a presence of hallucinations, they are rated higher than both the shared task winning system and human responses.

The latest advancements in AI and deep learning have led to a breakthrough in large language model (LLM)-based agents such as GPT-4. However, many commercial conversational agent development tools are pipeline-based and have limitations in holding a human-like conversation. This paper investigates the capabilities of LLMs to enhance pipeline-based conversational agents during two phases: 1) in the design and development phase and 2) during operations. In 1) LLMs can aid in generating training data, extracting entities and synonyms, localization, and persona design. In 2) LLMs can assist in contextualization, intent classification to prevent conversational breakdown and handle out-of-scope questions, auto-correcting utterances, rephrasing responses, formulating disambiguation questions, summarization, and enabling closed question-answering capabilities. We conducted informal experiments with GPT-4 in the private banking domain to demonstrate the scenarios above with a practical example. Companies may be hesitant to replace their pipeline-based agents with LLMs entirely due to privacy concerns and the need for deep integration within their existing ecosystems. A hybrid approach in which LLMs’ are integrated into the pipeline-based agents allows them to save time and costs of building and running agents by capitalizing on the capabilities of LLMs while retaining the integration and privacy safeguards of their existing systems.

Recent language models have been improved by the addition of external memory. Nearest neighbor language models retrieve similar contexts to assist in word prediction. The addition of locality levels allows a model to learn how to weight neighbors based on their relative location to the current text in source documents, and have been shown to further improve model performance. Nearest neighbor models have been explored for controllable generation but have not examined the use of locality levels. We present a novel approach for this purpose and evaluate it using automatic and human evaluation on politeness, formality, supportiveness, and toxicity textual data. We find that our model is successfully able to control style and provides a better fluency-style trade-off than previous work

Explanations accompanying a recommendation can assist users in understanding the decision made by recommendation systems, which in turn increases a user’s confidence and trust in the system. Recently, research has focused on generating natural language explanations in a human-readable format. Thus far, the proposed approaches leverage item reviews written by users, which are often subjective, sparse in language, and unable to account for new items that have not been purchased or reviewed before. Instead, we aim to generate fact-grounded recommendation explanations that are objectively described with item features while implicitly considering a user’s preferences, based on the user’s purchase history. To achieve this, we propose a knowledge graph (KG) approach to natural language explainable recommendation. Our approach draws on user-item features through a novel collaborative filtering-based KG representation to produce fact-grounded, personalized explanations, while jointly learning user-item representations for recommendation scoring. Experimental results show that our approach consistently outperforms previous state-of-the-art models on natural language explainable recommendation metrics.

How do sequence models represent their decision-making process? Prior work suggests that Othello-playing neural network learned nonlinear models of the board state (Li et al., 2023a). In this work, we provide evidence of a closely related linear representation of the board. In particular, we show that probing for “my colour” vs. “opponent’s colour” may be a simple yet powerful way to interpret the model’s internal state. This precise understanding of the internal representations allows us to control the model’s behaviour with simple vector arithmetic. Linear representations enable significant interpretability progress, which we demonstrate with further exploration of how the world model is computed.

Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks. We explore whether we can leverage this ability to find and explain patterns in data. Specifically, given a pre-trained LLM and data examples, we apply interpretable autoprompting (iPrompt) to generate a natural language string explaining the data. iPrompt iteratively generates explanations with an LLM and reranks them based on their performance when used as a prompt. Experiments on a wide range of datasets, from synthetic mathematics to natural language understanding, show that iPrompt can yield meaningful insights by accurately finding dataset explanations that are human-interpretable. Moreover, iPrompt is reasonably efficient, as it does not require access to model gradients and works with relatively small models (e.g. ~6 billion parameters rather than >=100 billion). Finally, experiments with scientific datasets show the potential for iPrompt to aid in scientific discovery.

With their increasing size, large language models (LLMs) are becoming increasingly good at language understanding tasks. But even with high performance on specific downstream task, LLMs fail at simple linguistic tests for negation or quantifier understanding. Previous work on quantifier understanding in LLMs show inverse scaling in understanding few-type quantifiers. In this paper, we question the claims of of previous work and show that it is a result of inappropriate testing methodology. We also present alternate methods to measure quantifier comprehension in LLMs and show that LLMs are able to better understand the difference between the meaning of few-type and most-type quantifiers as their size increases, although they are not particularly good at it. We also observe inverse scaling for most-type quantifier understanding, which is contrary to human psycho-linguistic experiments and previous work, where the model’s understanding of most-type quantifier gets worse as the model size increases. We do this evaluation on models ranging from 125M-175B parameters, which suggests that LLMs do not do as well as expected with quantifiers. We also discuss the possible reasons for this and the relevance of quantifier understanding in evaluating language understanding in LLMs.

Differential Privacy (DP) has been tailored to address the unique challenges of text-to-text privatization. However, text-to-text privatization is known for degrading the performance of language models when trained on perturbed text. Employing a series of interpretation techniques on the internal representations extracted from BERT trained on perturbed pre-text, we intend to disentangle at the linguistic level the distortion induced by differential privacy. Experimental results from a representational similarity analysis indicate that the overall similarity of internal representations is substantially reduced. Using probing tasks to unpack this dissimilarity, we find evidence that text-to-text privatization affects the linguistic competence across several formalisms, encoding localized properties of words while falling short at encoding the contextual relationships between spans of words.

The ubiquity of complex machine learning has raised the importance of model-agnostic explanation algorithms. These methods create artificial instances by slightly perturbing real instances, capturing shifts in model decisions. However, such methods rely on initial data and only provide explanations of the decision for these. To tackle these problems, we propose Therapy, the first global and model-agnostic explanation method adapted to text which requires no input dataset. Therapy generates texts following the distribution learned by a classifier through cooperative generation. Because it does not rely on initial samples, it allows to generate explanations even when data is absent (e.g., for confidentiality reasons). Moreover, conversely to existing methods that combine multiple local explanations into a global one, Therapy offers a global overview of the model behavior on the input space. Our experiments show that although using no input data to generate samples, Therapy provides insightful information about features used by the classifier that is competitive with the ones from methods relying on input samples and outperforms them when input samples are not specific to the studied model.

Large language models (LLMs) that do not give consistent answers across contexts are problematic when used for tasks with expectations of consistency–e.g. question-answering, explanations, etc. Our work presents an evaluation benchmark for self-consistency in cases of under-specification where two or more answers can be correct. We conduct a series of behavioral experiments on the OpenAI model suite using an ambiguous integer sequence completion task. We find that average consistency ranges from 67% to 82%, far higher than would be predicted if a model’s consistency was random, and increases as model capability improves. Furthermore, we show that models tend to maintain self-consistency across a series of robustness checks, including prompting speaker changes and sequence length changes. These results suggest that self-consistency arises as an emergent capability without specifically training for it. Despite this, we find that models are uncalibrated when judging their own consistency, with models displaying both over- and under-confidence. We also propose a nonparametric test for determining from token output distribution whether a model assigns non-trivial probability to alternative answers. Using this test, we find that despite increases in self-consistency, models usually place significant weight on alternative, inconsistent answers. This distribution of probability mass provides evidence that even highly self-consistent models internally compute multiple possible responses.

The Backpack is a Transformer alternative shown to improve interpretability in English language modeling by decomposing predictions into a weighted sum of token sense components. However, Backpacks’ reliance on token-defined meaning raises questions as to their potential for languages other than English, a language for which subword tokenization provides a reasonable approximation for lexical items. In this work, we train, evaluate, interpret, and control Backpack language models in character-tokenized Chinese, in which words are often composed of many characters. We find that our (134M parameter) Chinese Backpack language model performs comparably to a (104M parameter) Transformer, and learns rich character-level meanings that log-additively compose to form word meanings. In SimLex-style lexical semantic evaluations, simple averages of Backpack character senses outperform input embeddings from a Transformer. We find that complex multi-character meanings are often formed by using the same per-character sense weights consistently across context. Exploring interpretability-through control, we show that we can localize a source of gender bias in our Backpacks to specific character senses and intervene to reduce the bias.

Recent research suggests that the feed-forward module within Transformers can be viewed as a collection of key-value memories, where the keys learn to capture specific patterns from the input based on the training examples. The values then combine the output from the ‘memories’ of the keys to generate predictions about the next token. This leads to an incremental process of prediction that gradually converges towards the final token choice near the output layers. This interesting perspective raises questions about how multilingual models might leverage this mechanism. Specifically, for autoregressive models trained on two or more languages, do all neurons (across layers) respond equally to all languages? No! Our hypothesis centers around the notion that during pre-training, certain model parameters learn strong language-specific features, while others learn more language-agnostic (shared across languages) features. To validate this, we conduct experiments utilizing parallel corpora of two languages that the model was initially pre-trained on. Our findings reveal that the layers closest to the network’s input or output tend to exhibit more language-specific behaviour compared to the layers in the middle.

A recent body of work has demonstrated that Transformer embeddings can be linearly decomposed into well-defined sums of factors, that can in turn be related to specific network inputs or components. There is however still a dearth of work studying whether these mathematical reformulations are empirically meaningful. In the present work, we study representations from machine-translation decoders using two of such embedding decomposition methods. Our results indicate that, while decomposition-derived indicators effectively correlate with model performance, variation across different runs suggests a more nuanced take on this question. The high variability of our measurements indicate that geometry reflects model-specific characteristics more than it does sentence-specific computations, and that similar training conditions do not guarantee similar vector spaces.

The question of what kinds of linguistic information are encoded in different layers of Transformer-based language models is of considerable interest for the NLP community. Existing work, however, has overwhelmingly focused on word-level representations and encoder-only language models with the masked-token training objective. In this paper, we present experiments with semantic structural probing, a method for studying sentence-level representations via finding a subspace of the embedding space that provides suitable task-specific pairwise distances between data-points. We apply our method to language models from different families (encoder-only, decoder-only, encoder-decoder) and of different sizes in the context of two tasks, semantic textual similarity and natural-language inference. We find that model families differ substantially in their performance and layer dynamics, but that the results are largely model-size invariant.

Recent work suggests that large language models (LLMs) achieve higher accuracy on multi-step reasoning tasks when prompted to generate intermediate reasoning steps, or a chain of thought (CoT), before their final answer. However, it is unclear how exactly CoTs improve LLMs’ accuracy, and in particular, if LLMs use their CoTs to reason to their final answers. This paper tries to answer this question with respect to arithmetic word problems, by (i) evaluating the correctness of LLMs’ CoTs, and (ii) using causal abstraction to assess if the intermediate tokens produced as part of a CoT causally impact LLMs’ final answers, in line with the reasoning described by the CoT. We find that for CoT-prompted LLMs, correct answers to arithmetic problems are highly correlated with correct CoTs, and that when LLMs produce correct CoTs, they realize to a fairly large extent the causal models suggested by their CoTs. Higher degrees of realization also seem associated with better overall accuracy on the arithmetic problems. These findings suggest that some CoT-prompted LLMs may do better on multi-step arithmetic reasoning at least partly because they use their CoTs to reason to their final answers. However, for some LLMs, other internal processes may also be involved.

Interpretability methods in NLP aim to provide insights into the semantics underlying specific system architectures. Focusing on word embeddings, we present a supervised-learning method that, for a given domain (e.g., sports, professions), identifies a subset of model features that strongly improve prediction of human similarity judgments. We show this method keeps only 20-40% of the original embeddings, for 8 independent semantic domains, and that it retains different feature sets across domains. We then present two approaches for interpreting the semantics of the retained features. The first obtains the scores of the domain words (co-hyponyms) on the first principal component of the retained embeddings, and extracts terms whose co-occurrence with the co-hyponyms tracks these scores’ profile. This analysis reveals that humans differentiate e.g. sports based on how gender-inclusive and international they are. The second approach uses the retained sets as variables in a probing task that predicts values along 65 semantically annotated dimensions for a dataset of 535 words. The features retained for professions are best at predicting cognitive, emotional and social dimensions, whereas features retained for fruits or vegetables best predict the gustation (taste) dimension. We discuss implications for alignment between AI systems and human knowledge.

The increasing interest in probing the linguistic capabilities of large language models (LLMs) has long reached the area of semantics and pragmatics, including the phenomenon of presuppositions. In this study, we investigate a phenomenon that, however, has not yet been investigated, i.e., the phenomenon of anti-presupposition and the principle that accounts for it, the Maximize Presupposition! principle (MP!). Through an experimental investigation using psycholinguistic data and four open-source BERT model variants, we explore how language models handle different anti-presuppositions and whether they apply the MP! principle in their predictions. Further, we examine whether fine-tuning with Natural Language Inference data impacts adherence to the MP! principle. Our findings reveal that LLMs tend to replicate context-based n-grams rather than follow the MP! principle, with fine-tuning not enhancing their adherence. Notably, our results further indicate a striking difficulty of LLMs to correctly predict determiners, in relatively simple linguistic contexts.

Examples are a powerful tool that help us understand complex concepts and connections. In computational linguistics research, looking at example system output and example corpus entries can offer a wealth of insights that are not otherwise accessible. This paper describes the open-source software VULCAN, a visualization tool for strings, graphs, trees, alignments, attention and more. VULCAN’s unique ability to visualize both linguistic structures and properties of neural models make it particularly relevant for neuro-symbolic models. Neuro-symbolic models, combining neural networks with often linguistically grounded structures, offer a promise of increased interpretability in an age of purely neural black-box end-to-end models. VULCAN aims to facilitate this interpretability in practice. VULCAN is designed to be both easy to use and powerful in its capabilities.

Several methodologies have recently been proposed to evaluate the ability of Pretrained Language Models (PLMs) to interpret negation. In this article, we build on Gubelmann and Handschuh (2022), which studies the modification of PLMs’ predictions as a function of the polarity of inputs, in English. Crucially, this test uses “self-contained” inputs ending with a masked position: depending on the polarity of a verb in the input, a particular token is either semantically ruled out or allowed at the masked position. By replicating Gubelmann and Handschuh (2022) experiments, we have uncovered flaws that weaken the conclusions that can be drawn from this test. We thus propose an improved version, the Self-Contained Neg Test, which is more controlled, more systematic, and entirely based on examples forming minimal pairs varying only in the presence or absence of verbal negation in English. When applying our test to the roberta and bert base and large models, we show that only roberta-large shows trends that match the expectations, while bert-base is mostly insensitive to negation. For all the tested models though, in a significant number of test instances the top-1 prediction remains the token that is semantically forbidden by the context, which shows how much room for improvement remains for a proper treatment of the negation phenomenon.

As neural language models (NLMs) based on Transformers are becoming increasingly dominant in natural language processing, several studies have proposed analyzing the semantic and pragmatic abilities of such models. In our study, we aimed at investigating the effect of discourse connectives on NLMs with regard to Transformer Surprisal scores by focusing on the English stimuli of an experimental dataset, in which the expectations about an event in a discourse fragment could be reversed by a concessive or a contrastive connective. By comparing the Surprisal scores of several NLMs, we found that bigger NLMs show patterns similar to humans’ behavioral data when a concessive connective is used, while connective-related effects tend to disappear with a contrastive one. We have additionally validated our findings with GPT-Neo using an extended dataset, and results mostly show a consistent pattern.

Probing contextualized representations typically involves comparing task-specific model predictions against ground truth linguistic labels. Although this methodology shows what information can be recovered by a classifier, it does not reveal how a classifier uses the representation to make its decision. To address the latter problem, we ask: Do task-classifiers rely on representation- and task-independent geometric patterns in the embedding space? We explore this question by developing MetaProbe, an approach that uses geometric properties of representations to predict the behavior of task-specific classifiers (i.e., their predictions as opposed to the ground truth). Our experiments reveal the existence of universal geometric patterns across representations that can predict classifier predictions. Consequently, this allows us to posit a geometric explanation for the impressive performance of contextualized representations.

Contrast set consistency is a robustness measurement that evaluates the rate at which a model correctly responds to all instances in a bundle of minimally different examples relying on the same knowledge. To draw additional insights, we propose to complement consistency with relative consistency—the probability that an equally accurate model would surpass the consistency of the proposed model, given a distribution over possible consistencies. Models with 100% relative consistency have reached a consistency peak for their accuracy. We reflect on prior work that reports consistency in contrast sets and observe that relative consistency can alter the assessment of a model’s consistency compared to another. We anticipate that our proposed measurement and insights will influence future studies aiming to promote consistent behavior in models.

Pretrained language models (PLMs) form the basis of most state-of-the-art NLP technologies. Nevertheless, they are essentially black boxes: Humans do not have a clear understanding of what knowledge is encoded in different parts of the models, especially in individual neurons. A contrast is in computer vision, where feature visualization provides a decompositional interpretability technique for neurons of vision models. Activation maximization is used to synthesize inherently interpretable visual representations of the information encoded in individual neurons. Our work is inspired by this but presents a cautionary tale on the interpretability of single neurons, based on the first large-scale attempt to adapt activation maximization to NLP, and, more specifically, large PLMs. We propose feature textualization, a technique to produce dense representations of neurons in the PLM word embedding space. We apply feature textualization to the BERT model to investigate whether the knowledge encoded in individual neurons can be interpreted and symbolized. We find that the produced representations can provide insights about the knowledge encoded in individual neurons, but that individual neurons do not represent clear-cut symbolic units of language such as words. Additionally, we use feature textualization to investigate how many neurons are needed to encode words in BERT.

Despite the fact that Transformers perform well in NLP tasks, recent studies suggest that self-attention is theoretically limited in learning even some regular and context-free languages. These findings motivated us to think about their implications in modeling natural language, which is hypothesized to be mildly context-sensitive. We test the Transformer’s ability to learn mildly context-sensitive languages of varying complexities, and find that they generalize well to unseen in-distribution data, but their ability to extrapolate to longer strings is worse than that of LSTMs. Our analyses show that the learned self-attention patterns and representations modeled dependency relations and demonstrated counting behavior, which may have helped the models solve the languages.

Large language models (LLMs) like GPT and PALM have excelled in numerous natural language processing (NLP) tasks such as text generation, question answering, and translation. However, they are also found to have inherent social biases. To address this, recent studies have proposed debiasing techniques like iterative nullspace projection (INLP) and Counterfactual Data Augmentation (CDA). Additionally, there’s growing interest in understanding the intricacies of these models. Some researchers focus on individual neural units, while others examine specific layers. In our study, we benchmark newly released models, assess the impact of debiasing methods, and investigate how biases are linked to different transformer layers using a method called Logit Lens. Specifically, we evaluate three modern LLMs: OPT, LLaMA, and LLaMA2, and their debiased versions. Our experiments are based on two popular bias evaluation datasets, StereoSet and CrowS-Pairs, and we perform a layer-by-layer analysis using the Logit Lens.

Vector-space models of word meaning all assume that words occurring in similar contexts have similar meanings. Words that are similar in their topical associations but differ in their logical force tend to emerge as semantically close – creating well-known challenges for NLP applications that involve logical reasoning. Pretrained language models such as BERT, RoBERTa, GPT-2, and GPT-3 hold the promise of performing better on logical tasks than classic static word embeddings. However, reports are mixed about their success. Here, we advance this discussion through a systematic study of scalar adverbs, an under-explored class of words with strong logical force. Using three different tasks involving both naturalistic social media data and constructed examples, we investigate the extent to which BERT, RoBERTa, GPT-2 and GPT-3 exhibit knowledge of these common words. We ask: 1) Do the models distinguish amongst the three semantic categories of MODALITY, FREQUENCY and DEGREE? 2) Do they have implicit representations of full scales from maximally negative to maximally positive? 3) How do word frequency and contextual factors impact model performance? We find that despite capturing some aspects of logical meaning, the models still have obvious shortfalls.

Natural language is an appealing medium for explaining how large language models process and store information, but evaluating the faithfulness of such explanations is challenging. To help address this, we develop two modes of evaluation for natural language explanations that claim individual neurons represent a concept in a text input. In the *observational mode*, we evaluate claims that a neuron a activates on all and only input strings that refer to a concept picked out by the proposed explanation E. In the *intervention mode*, we construe E as a claim that neuron a is a causal mediator of the concept denoted by E. We apply our framework to the GPT-4-generated explanations of GPT-2 XL neurons of Bills et al. (2023) and show that even the most confident explanations have high error rates and little to no causal efficacy. We close the paper by critically assessing whether natural language is a good choice for explanations and whether neurons are the best level of analysis.

We examine the licensing of negative polarity items (NPIs) in large language models (LLMs) to enrich the picture of how models acquire NPIs as linguistic phenomena at the syntax-semantics interface. NPIs are a class of words which have a restricted distribution, appearing only in certain licensing contexts, prototypically negation. Unlike much of previous work which assumes NPIs and their licensing environments constitute unified classes, we consider NPI distribution in its full complexity: different NPIs are possible in different licensing environments. By studying this phenomenon across a broad range of models, we are able to explore which features of the model architecture, properties of the training data, and linguistic characteristics of the NPI phenomenon itself drive performance.

Answering multi-hop reasoning questions requires retrieving and synthesizing information from diverse sources. Large Language Models (LLMs) struggle to perform such reasoning consistently. Here we propose an approach to pinpoint and rectify multi-hop reasoning failures through targeted memory injections on LLM attention heads. First, we analyze the per-layer activations of GPT-2 models in response to single and multi-hop prompts. We then propose a mechanism that allows users to inject pertinent prompt-specific information, which we refer to as “memories,” at critical LLM locations during inference. By thus enabling the LLM to incorporate additional relevant information during inference, we enhance the quality of multi-hop prompt completions. We show empirically that a simple, efficient, and targeted memory injection into a key attention layer can often increase the probability of the desired next token in multi-hop tasks, by up to 424%.

Constituents are groups of words that behave as a syntactic unit. Many linguistic phenomena (e.g., question formation, diathesis alternations) require the manipulation and rearrangement of constituents in a sentence. In this paper, we investigate how different finetuning setups affect the ability of pretrained sequence-to-sequence language models such as BART and T5 to replicate constituency tests — transformations that involve manipulating constituents in a sentence. We design multiple evaluation settings by varying the combinations of constituency tests and sentence types that a model is exposed to during finetuning. We show that models can replicate a linguistic transformation on a specific type of sentence that they saw during finetuning, but performance degrades substantially in other settings, showing a lack of systematic generalization. These results suggest that models often learn to manipulate sentences at a surface level unrelated to the constituent-level syntactic structure, for example by copying the first word of a sentence. These results may partially explain the brittleness of pretrained language models in downstream tasks.

Psycholinguistic studies suggested that our mental perception of events depends not only on the lexical items used to describe them, but also on the syntactic structure of the event description. More specifically, it has been argued that light verb constructions affect the perception of duration in event construal, such that the same event in this type of constructions is perceived by humans as taking less time (to give a kiss takes a shorter time than to kiss). In our paper, we present two experiments with BERT using English stimuli from psycholinguistic studies to investigate the effects of the syntactic construction on event duration and event similarity. We show that i) the dimensions of BERT vectors encode a smaller value for duration for both punctive and durative events in count syntax, in line with human results; on the other hand, we also found that ii) BERT semantic similarity fails to capture the conceptual shift that durative events should undergo in count syntax.

Language models (LMs) exhibit and amplify many types of undesirable biases learned from the training data, including gender bias. However, we lack tools for effectively and efficiently changing this behavior without hurting general language modeling performance. In this paper, we study three methods for identifying causal relations between LM components and particular output: causal mediation analysis, automated circuit discovery and our novel, efficient method called DiffMask+ based on differential masking. We apply the methods to GPT-2 small and the problem of gender bias, and use the discovered sets of components to perform parameter-efficient fine-tuning for bias mitigation. Our results show significant overlap in the identified components (despite huge differences in the computational requirements of the methods) as well as success in mitigating gender bias, with less damage to general language modeling compared to full model fine-tuning. However, our work also underscores the difficulty of defining and measuring bias, and the sensitivity of causal discovery procedures to dataset choice. We hope our work can contribute to more attention for dataset development, and lead to more effective mitigation strategies for other types of bias.

Position paper for YRRSDS 2023

In this position paper, I will present the research interests in my PostDoc on safety and robustness specific to conversational AI, including then relevant overlap from my PhD.

Speech production is nuanced and unique to every individual, but today’s Spoken Dialogue Systems (SDSs) are trained to use general speech patterns to successfully improve performance on various evaluation metrics. However, these patterns do not apply to certain user groups - often the very people that can benefit the most from SDSs. For example, people with dementia produce more disfluent speech than the general population. The healthcare domain is now a popular setting for spoken dialogue and human-robot interaction research. This trend is similar when observing company behaviour. Charities promote industry voice assistants, the creators are getting HIPAA compliance, and their features sometimes target vulnerable user groups. It is therefore critical to adapt SDSs to be more accessible.

This research encompasses a comprehensive exploration of Spoken Dialogue Systems (SDSs) in the manufacturing sector. It begins by establishing a conceptual architecture and taxonomy to guide the design and selection of SDS elements. Real case applications, including worker safety and cybersecurity support, validate the research findings and highlight areas for improvement. Looking ahead, the study delves into the potential of Large Language Models (LLMs) and multi-modal applications. Emphasizing the importance of extreme personalization, the study highlights the need to cater to the diverse qualifications and preferences of workers. Additionally, it investigates the integration of SDSs with other sensory modalities, such as images, videos, and augmented or virtual reality scenarios, to enhance the user experience and productivity. The research also addresses crucial considerations related to knowledge base optimization. It examines semantic variations of words across different application contexts, the continuous updating of procedures and data, and the adaptability of SDSs to diverse dialects and linguistic abilities, particularly in low-schooling personnel scenarios. Privacy, industrial protection, and ethical concerns in the era of LLMs and external players like OpenAI are given due attention. The study explores the boundaries of knowledge that conversational systems should possess, advocating for transparency, explainability, and responsible data handling practices.

The process of “conversational grounding” is an interactive process that has been studied extensively in cognitive science, whereby participants in a conversation check to make sure their interlocutors understand what is being referred to. This interactive process uses multiple modes of communication to establish the information between the participants. This could include information provided through eye-gaze, head movements, intonation in speech, along with the content of the speech. While the process is essential to successful communication between humans and between humans and machines, work needs to be done on testing and building the capabilities of the current dialogue system in managing conversational grounding, especially in multimodal medium of communication. Recent work such as Benotti and Blackburn have shown the importance of conversational grounding in dialog systems and how current systems fail in them. This is essential for the advancement of Embodied Conversational Agents and Social Robots. Thus my PhD project aims to test, understand and improve the functioning of current dialog models with respect to Conversational Grounding.

My research interests focus on natural language generation (NLG) regarding how to make system outputs more intuitive and comprehensible for the human-user and conversational entrainment and alignment from the perspective of how dialogue systems could or should personalize its responses to the human user. As it relates to NLG, my current work focuses on training a system to auto-generate comments for SQL queries produced by a Text-to-SQL parser. The goal is to make the connection between technical SQL language and the user’s question more transparent. My linguistic training lies primarily at the intersection of computational and socio-linguistics. As such, my curiosities in conversational entrainment and alignment focus on the extent to which conversational agents can or should adjust their language based on human characteristics such as age, race, or gender.

Position paper for YRRSDS 2023

Large language models (LLMs) have brought about a significant transformation in spoken dialogue systems (SDSs). It is anticipated that these systems will be implemented into diverse robotic applications and employed in a variety of social settings. The author presents research interest with the aim of realizing social SDSs from multiple perspectives, including task design, turn-taking mechanisms, and evaluation methodologies. Additionally, future research in social SDSs should delve into a deeper understanding of user mental states and a relationship with society via multi-party conversations. Finally, the author suggests topics for discussion regarding the future directions of SDS researchers in the LLM era.

Many companies use dialogue systems for their customer service, and although there has been a rise in the usage of these systems (Costello and LoDolce, 2022), many of these systems still face challenges in comprehending and properly responding to the customer (Følstadet al., 2021). In our project we aim to figure out how to develop and improve these conversational agents. Part of this project (detailed in this paper) will focus on the detection of breakdown patterns and the possible solutions (repairs) to mitigate negative results of these errors.

Position paper on the intersection between chitchat and task-oriented dialogues (TODs), with a focus on integrating capabilities typically associated with chitchat systems into task-oriented agents.

This is my position paper for YRRSDS 2023. In it, I write about the details of my research interests as well as past, current and future projects, talk about the status of spoken dialogue system research, include a short bio, and suggest topics for discussion.

This position paper is an overview of author’s main research interests and work considering deep learning techniques in audio classification, sign languages, and multimodality in dialogue systems. Author also shares her opinion on current and future research considering dialogue agents, and suggests topics for discussion panels.

Task-Oriented Dialogue (TOD) systems provide interactive assistance to a user in order to accomplish a specific task such as making a reservation at a restaurant or booking a room in a hotel. Speech presents itself as a natural interface for TOD systems. A typical approach to implement them is to use a modular architecture (Gao et al., 2018). A core component of such dialogue systems is Spoken Language Understanding (SLU) whose goal is to extract the relevant information from the user’s utterances. While spoken dialogue was the focus of earlier work (Williams et al., 2013; Henderson et al., 2014), recent work has focused on text inputs with no regard for the specificities of spoken language (Wu et al., 2019; Heck et al., 2020; Feng et al., 2021). However, this approach fails to account for the differences between written and spoken language (Faruqui and Hakkani-Tür, 2022) such as disfluencies. My research focuses on Spoken Language Understanding in the context of Task-Oriented Dialogue. More specifically I am interested in the two following research directions: • Annotation schema for spoken TODs, • Integration of dialogue history for contextually coherent predictions.

My position paper for the YRRSDS 2023 workshop.

My PhD focuses on conversational agents for behaviour change, with a focus on the feasibility of applying Large Language Models (LLMs) such as GPT-4 in this context.

My primary research focus lies in the domain of Text Style Transfer (TST), a fascinating area within Natural Language Processing (NLP). TST involves the transfor- mation of text into a desired style while approximately preserving its underlying content. In my research, I am also driven by the goal of incorporating TST techniques into NLP systems, particularly within the realm of dia- logue systems. I am intrigued by the concept of Stylized Dialog Response Generation, which aims to enhance the versatility and adaptability of dialog systems in generat- ing text responses with specific style attributes. By ad- vancing our understanding of TST and its integration into dialogue systems, my research seeks to contribute to the broader field of human-computer interaction. Through the development of robust and versatile dialogue systems with enhanced style transfer capabilities, we can facili- tate more engaging and personalized conversational experiences.

A brief introduction to author’s keyinterests and research topics which are: multimodal dialogue systems and impact of data augmentation to NLU performance. In addition to that the author shares his biography and view on the future of dialogue assistants.

This submission discusses my research interests in two areas: measuring user satisfaction in goal-oriented dialogue systems and exploring the potential of multi-modal interactions. For goal-oriented dialogue systems, I focus on evaluating and enhancing user satisfaction throughout the interaction process, aiming to propose innovative strategies and address the limitations of existing evaluation techniques. Additionally, I explore the benefits of multi-modal dialogue systems, highlighting their ability to provide more natural and immersive conversations by incorporating various communication modes such as speech, text, gestures, and visuals.

My research interests broadly lie in the area of Information Extraction from Spoken Dialogue, with a spacial focus on state modeling, anaphora resolution, program synthesis & planning, and intent classification in goal-oriented conversations. My aim is to create embedded dialogue systems that can interact with humans in a collaborative setup to solve tasks in a digital/non-digital environment. Most of the goal-oriented conversations usually involve experts and a laypersons. The aim for the expert is to consider all the information provided by the layperson, identify the underlying set of issues or intents, and prescribe solutions. While human experts are very good at extracting such information, AI agents (that build up most of the automatic dialog systems today) not so much. Most of the existing assistants (or chatbots) only consider individual utterances and do not ground them in the context of the dialogue. My work in this direction has focused on making these systems more effective at extracting the most relevant information from the dialogue to help the human user reach their end-goal.

My research interests lie in the area of modelling natural and human-like conversations, with a special focus on emotions in task-oriented dialogue (ToD) systems. ToD systems need to produce semantically and grammatically correct responses to fulfil the user’s goal. Being able to perceive and express emotions pushes them one more step towards achieving human-likeness. To begin with, I constructed a dataset with meaningful emotion labels as well as a wide coverage of emotions and linguistic features in ToDs. Then, I improved emotion recognition in conversations (ERC) in the task-oriented domain by exploiting key characteristics of ToDs. Currently, I am working towards enhancing ToD systems with emotions.

I am broadly interested in evaluation of dialogue systems, in all its many facets: The data they are trained on, their ability to perform a task successfully, their skills with respect to various dialogue phenomena, their resemblance to human cognitive processes, and their ethical and societal impact. More specifically, my research topics focus on understanding the possibilities and limits of current multimodal neural network-based models to incrementally encode information for natural language understanding in general and also for building common ground and asking for clarification. Besides, I am interested in dialogue games as a means to elicit and collect dialogue data and to evaluate the abilities of dialogue models.

My research work centers on how to enable a human-like interaction through generating contextual, emotional or proactive responses, both in task-oriented and in chitchat spoken dialogue systems (SDSs), because natural lan- guage generation (NLG) is an indispensable component in SDSs and can directly affect the user interactive expe- rience of the entire dialogue system. In addition to NLG, I am also interested in natural language understanding (NLU), as it plays a crucial role in SDSs and is a prerequisite for dialogue systems to generate replies.

The author’s objective centers around developing a spoken dialogue system (SDS) that can emulate the cognitive and conversational qualities of a human friend. Key attributes such as empathy, knowledge/causality reasoning, and personality are integral components of human interaction. The proposed approach involves the creation of an Empathy-enriched SDS, capable of comprehending human emotions and circumstances, thus providing companionship and assistance akin to a trusted friend. Additionally, the Causality-reasoning for SDS aims to ground the system in commonsense knowledge and equip it with the ability to reason about causalities, such as predicting user desires/reactions and system intentions/reactions, thereby enhancing the system’s intelligence and human-like behavior. Finally, the concept of a Personality-conditioned SDS involves enabling systems to exhibit distinct personalities, further enhancing the naturalness of human-robot interaction.

This position paper describes my research interests, spoken dialogue system research, and suggested topics for discussion.

In recent times, offensive span identification in code-mixed Tamil-English language has seen traction with the release of datasets, shared tasks, and the development of multiple methods. However, the details of various errors shown by these methods are currently unclear. This paper presents a detailed analysis of various errors in state-of-the-art Tamil-English offensive span identification methods. Our study reveals the strengths and weaknesses of the widely used sequence labeling and zero-shot models for offensive span identification. In the due process, we identify data-related errors, improve data annotation and release additional diagnostic data to evaluate models’ quality and stability. Disclaimer: This paper contains examples that may be considered profane, vulgar, or offensive. The examples do not represent the views of the authors or their employers/graduate schools towards any person(s), group(s), practice(s), or entity/entities. Instead, they emphasize the complexity of various errors and linguistic research challenges.

This paper focuses on identifying hate and offensive keywords from codemix Malayalam social media text. As part of this work, a dataset for hate and offensive keyword extraction for codemix Malayalam language was created. Two different methods were experimented to extract Hate and Offensive language (HOL) keywords from social media text. In the first method, intrinsic evaluation was performed on the dataset to identify the hate and offensive keywords. Three different approaches namely – unigram approach, bigram approach and trigram approach were performed to extract the HOL keywords, sequence of HOL words and the sequence that contribute HOL meaning even in the absence of a HOL word. Five different transformer models were used in each of the pproaches for extracting the embeddings for the ngrams. Later, HOL keywords were extracted based on the similarity score obtained using the cosine similarity. Out of the five transformer models, the best results were obtained with multilingual BERT. In the second method, multilingual BERT transformer model was fine tuned with the dataset to develop a HOL keyword tagger model. This work is a new beginning for HOL keyword identification in Dravidian language – Malayalam.

This paper investigates the claim of rhoticity of the fifth liquid in Malayalam using various acoustic characteristics. The Malayalam liquid phonemes are analyzed in terms of the smoothness of the pitch window, formants, formant bandwidth, the effect on surrounding vowels, duration, and classification patterns by an unrelated classifier. We report, for the fifth liquid, a slight similarity in terms of pitch smoothness with one of the laterals, similarity with the laterals in terms of F1 for males, and similarity with the laterals and one of the rhotics in terms of F1 for females. The similarity in terms of formant bandwidth between the fifth liquid and the other liquids is inconclusive. Similarly, the effect of the fifth liquid on the surrounding vowels is inconclusive. No similarity is observed between the fifth liquid and the other liquids in phoneme duration. Classification of the fifth liquid section implies higher order signal level similarity with both laterals and rhotics.

This paper addresses the challenges faced by Indian languages in leveraging deep learning for natural language processing (NLP) due to limited resources, annotated datasets, and Transformer-based architectures. We specifically focus on Telugu and aim to construct a Telugu morph analyzer dataset comprising 10,000 sentences. Furthermore, we assess the performance of established multi-lingual Transformer models (m-Bert, XLM-R, IndicBERT) and mono-lingual Transformer models trained from scratch on an extensive Telugu corpus comprising 80,15,588 sentences (BERT-Te). Our findings demonstrate the efficacy of Transformer-based representations pretrained on Telugu data in improving the performance of the Telugu morph analyzer, surpassing existing multi-lingual approaches. This highlights the necessity of developing dedicated corpora, annotated datasets, and machine learning models in a mono-lingual setting. We present benchmark results for the Telugu morph analyzer achieved through simple fine-tuning on our dataset.

Reinforcement learning (RL) agents have achieved remarkable success in various domains, such as game-playing and protein structure prediction. However, most RL agents rely on exploration to find optimal solutions without explicit guidance. This paper proposes a methodology for training RL agents using text-based instructions in Dravidian Languages, including Telugu, Tamil, and Malayalam along with using the English language. The agents are trained in a modified Lunar Lander environment, where they must follow specific paths to successfully land the lander. The methodology involves collecting a dataset of human demonstrations and textual instructions, encoding the instructions into numerical representations using text-based embeddings, and training RL agents using state-of-the-art algorithms. The results demonstrate that the trained Soft Actor-Critic (SAC) agent can effectively understand and generalize instructions in different languages, outperforming other RL algorithms such as Proximal Policy Optimization (PPO) and Deep Deterministic Policy Gradient (DDPG).

In this paper, we present a study on social media data analysis of Malayalam YouTube comments, specifically focusing on sentiment analysis and emotion detection. Our research aims to investigate the effectiveness of various machine learning (ML) and deep learning (DL) models in addressing these two tasks. For sentiment analysis, we collected a dataset consisting of 3064 comments, while for two-class emotion detection, we used a dataset of 817 comments. In the sentiment analysis phase, we explored multiple ML and DL models, including traditional algorithms such as Support Vector Machines (SVM), Naïve Bayes, K-Nearest Neighbors (KNN), MLP Classifier, Decision Tree, and Random Forests. Additionally, we utilized DL models such as Recurrent Neural Networks (RNN), LSTM, and GRU. To enhance the performance of these models, we preprocessed the Malayalam YouTube comments by tokenizing and removing stop words. Experimental results revealed that DL models achieved higher accuracy compared to ML models, indicating their ability to capture the complex patterns and nuances in the Malayalam language. Furthermore, we extended our analysis to emotion detection, which involved dealing with limited annotated data. This task is closely related to social media data analysis. For emotion detection, we employed the same ML models used in the sentiment analysis phase. Our dataset of 817 comments was annotated with two emotions: Happy and Sad. We trained the models to classify the comments into these emotion classes and analyzed the accuracy of the different models.

Maintaining effective control over offensive content is essential on social media platforms to foster constructive online discussions. Yet, when it comes to code-mixed Dravidian languages, the current prevalence of offensive content moderation is restricted to categorizing entire comments, failing to identify specific portions that contribute to the offensiveness. Such limitation is primarily due to the lack of annotated data and open source systems for offensive spans. To alleviate this issue, in this shared task, we offer a collection of Tamil-English code-mixed social comments that include offensive comments. This paper provides an overview of the released dataset, the algorithms employed, and the outcomes achieved by the systems submitted for this task.

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In recent years, there has been a growing focus on Sentiment Analysis (SA) of code-mixed Dravidian languages. However, the majority of social media text in these languages is code-mixed, presenting a unique challenge. Despite this, there is currently lack of research on SA specifically tailored for code-mixed Dravidian languages, highlighting the need for further exploration and development in this domain. In this view, “Sentiment Analysis in Tamil and Tulu- DravidianLangTech” shared task at Recent Advances in Natural Language Processing (RANLP)- 2023 is organized. This shred consists two language tracks: code-mixed Tamil and Tulu and Tulu text is first ever explored in public domain for SA. We describe the task, its organization, and the submitted systems followed by the results. 57 research teams registered for the shared task and We received 27 systems each for code-mixed Tamil and Tulu texts. The performance of the systems (developed by participants) has been evaluated in terms of macro average F1 score. The top system for code-mixed Tamil and Tulu texts scored macro average F1 score of 0.32, and 0.542 respectively. The high quality and substantial quantity of submissions demonstrate a significant interest and attention in the analysis of code-mixed Dravidian languages. However, the current state of the art in this domain indicates the need for further advancements and improvements to effectively address the challenges posed by code-mixed Dravidian language SA.

This paper summarizes the shared task on multimodal abusive language detection and sentiment analysis in Dravidian languages as part of the third Workshop on Speech and Language Technologies for Dravidian Languages at RANLP 2023. This shared task provides a platform for researchers worldwide to submit their models on two crucial social media data analysis problems in Dravidian languages - abusive language detection and sentiment analysis. Abusive language detection identifies social media content with abusive information, whereas sentiment analysis refers to the problem of determining the sentiments expressed in a text. This task aims to build models for detecting abusive content and analyzing fine-grained sentiment from multimodal data in Tamil and Malayalam. The multimodal data consists of three modalities - video, audio and text. The datasets for both tasks were prepared by collecting videos from YouTube. Sixty teams participated in both tasks. However, only two teams submitted their results. The submissions were evaluated using macro F1-score.

This paper discusses the submissions to the shared task on abusive comment detection in Tamil and Telugu codemixed social media text conducted as part of the third Workshop on Speech and Language Technologies for Dravidian Languages at RANLP 20239. The task encourages researchers to develop models to detect the contents containing abusive information in Tamil and Telugu codemixed social media text. The task has three subtasks - abusive comment detection in Tamil, Tamil-English and Telugu-English. The dataset for all the tasks was developed by collecting comments from YouTube. The submitted models were evaluated using macro F1-score, and prepared the rank list accordingly.

We present CoPara, the first publicly available paragraph-level (n-way aligned) multilingual parallel corpora for Dravidian languages. The collection contains 2856 paragraph/passage pairs between English and four Dravidian languages. We source the parallel paragraphs from the New India Samachar magazine and align them with English as a pivot language. We do human and artificial evaluations to validate the high-quality alignment and richness of the parallel paragraphs of a range of lengths. To show one of the many ways this dataset can be wielded, we finetuned IndicBART, a seq2seq NMT model on all XX-En pairs of languages in CoPara which perform better than existing sentence-level models on standard benchmarks (like BLEU) on sentence level translations and longer text too. We show how this dataset can enrich a model trained for a task like this, with more contextual cues and beyond sentence understanding even in low-resource settings like that of Dravidian languages. Finally, the dataset and models are made available publicly at CoPara to help advance research in Dravidian NLP, parallel multilingual, and beyond sentence-level tasks like NMT, etc.

This research investigates the effectiveness of Chat- GPT, an AI language model by OpenAI, in translating English into Hindi, Telugu, and Kannada languages, aimed at assisting tourists in India’s linguistically diverse environment. To measure the translation quality, a test set of 50 questions from diverse fields such as general knowledge, food, and travel was used. These were assessed by five volunteers for accuracy and fluency, and the scores were subsequently converted into a BLEU score. The BLEU score evaluates the closeness of a machine-generated translation to a human translation, with a higher score indicating better translation quality. The Hindi translations outperformed others, showcasing superior accuracy and fluency, whereas Telugu translations lagged behind. Human evaluators rated both the accuracy and fluency of translations, offering a comprehensive perspective on the language model’s performance.

As one of the most extensively used languages in India, Telugu has a sizable audience and a huge library of news articles. Predicting the categories of Telugu news items not only helps with efficient organization but also makes it possible to do trend research, advertise in a certain demographic, and provide individualized recommendations. In order to identify the most effective method for accurate Telugu news category prediction, this study compares and contrasts various machine learning (ML) techniques, including support vector machines (SVM), random forests, and naive Bayes. Accuracy, precision, recall, and F1-score will be utilized as performance indicators to gauge how well these algorithms perform. The outcomes of this comparative analysis will address the particular difficulties and complexities of the Telugu language and add to the body of knowledge on news category prediction. For Telugu-speaking consumers, the study intends to improve news organization and recommendation systems, giving them more relevant and customized news consumption experiences. Our result emphasize that, although other models can be taken into account for further research and comparison, W2Vec-skip gram with polynomial SVM is the best performing combination.

Automatic Speech Recognition and its applications are rising in popularity across applications with reasonable inference results. Recent state-of-the-art approaches, often employ significantly large-scale models to show high accuracy for ASR as a whole but often do not consider detailed analysis of performance across low-resource languages applications. In this preliminary work, we propose to revisit ASR in the context of Connected Number Recognition (CNR). More specifically, we (i) present a new dataset HCNR collected to understand various errors of ASR models for CNR, (ii) establish preliminary benchmark and baseline model for CNR, (iii) explore error mitigation strategies and their after-effects on CNR. In the due process, we also compare with end-to-end large scale ASR models for reference, to show its effectiveness.

Sentiment analysis in code-mixed languages poses significant challenges, particularly for highly under-resourced languages such as Tulu and Tamil. Existing corpora, primarily sourced from YouTube comments, suffer from class imbalance across sentiment categories. Moreover, the limited number of samples in these corpus hampers effective sentiment classification. This study introduces a new corpus tailored for sentiment analysis in Tulu code-mixed texts. The research applies standard pre-processing techniques to ensure data quality and consistency and handle class imbalance. Subsequently, multiple classifiers are employed to analyze the sentiment of the code-mixed texts, yielding promising results. By leveraging the new corpus, the study contributes to advancing sentiment analysis techniques in under-resourced code-mixed languages. This work serves as a stepping stone towards better understanding and addressing the challenges posed by sentiment analysis in highly under-resourced languages.

The proposed system procures a systematic workflow in fake news identification utilizing machine learning classification in order to recognize and distinguish between real and made-up news. Using the Natural Language Toolkit (NLTK), the procedure starts with data preprocessing, which includes operations like text cleaning, tokenization, and stemming. This guarantees that the data is translated into an analytically-ready format. The preprocessed data is subsequently supplied into transformer models like M-BERT, Albert, XLNET, and BERT. By utilizing their extensive training on substantial datasets to identify complex patterns and significant traits that discriminate between authentic and false news pieces, these transformer models excel at capturing contextual information. The most successful model among those used is M-BERT, which boasts an astounding F1 score of 0.74. This supports M-BERT’s supremacy over its competitors in the field of fake news identification, outperforming them in terms of performance. The program can draw more precise conclusions and more effectively counteract the spread of false information because of its comprehension of contextual nuance. Organizations and platforms can strengthen their fake news detection systems and their attempts to stop the spread of false information by utilizing M-BERT’s capabilities.

Detecting abusive language in multimodal videos has become a pressing need in ensuring a safe and inclusive online environment. This paper focuses on addressing this challenge through the development of a novel approach for multimodal abusive language detection in Tamil videos and sentiment analysis for Tamil/Malayalam videos. By leveraging state-of-the-art models such as Multiscale Vision Transformers (MViT) for video analysis, OpenL3 for audio analysis, and the bert-base-multilingual-cased model for textual analysis, our proposed framework integrates visual, auditory, and textual features. Through extensive experiments and evaluations, we demonstrate the effectiveness of our model in accurately detecting abusive content and predicting sentiment categories. The limited availability of effective tools for performing these tasks in Dravidian Languages has prompted a new avenue of research in these domains.

The amount of digital material that is disseminated through various social media platforms has significantly increased in recent years. Online networks have gained popularity in recent years and have established themselves as goto resources for news, information, and entertainment. Nevertheless, despite the many advantages of using online networks, mounting evidence indicates that an increasing number of malicious actors are taking advantage of these networks to spread poison and hurt other people. This work aims to detect abusive content in youtube comments written in the languages like Tamil, Tamil-English (codemixed), Telugu-English (code-mixed). This work was undertaken as part of the “DravidianLangTech@ RANLP 2023” shared task. The Macro F1 values for the Tamil, Tamil-English, and Telugu-English datasets were 0.28, 0.37, and 0.6137 and secured 5th, 7th, 8th rank respectively.

Sentiment analysis in natural language processing (NLP), endeavors to computationally identify and extract subjective information from textual data. In code-mixed text, sentiment analysis presents a unique challenge due to the mixing of languages within a single textual context. For low-resourced languages such as Tamil and Tulu, predicting sentiment becomes a challenging task due to the presence of text comprising various scripts. In this research, we present the sentiment analysis of code-mixed Tamil and Tulu Youtube comments. We have developed a Bidirectional Long-Short Term Memory (BiLSTM) networks based models for both languages which further uses contextualized word embeddings at input layers of the models. For that purpose, ELMo embeddings have been trained on larger unannotated code-mixed text like corpora. Our models performed with macro average F1-scores of 0.2877 and 0.5133 on Tamil and Tulu code-mixed datasets respectively.

Millions of posts and comments are created every minute as a result of the widespread use of social media and easy access to the internet.It is essential to create an inclusive environment and forbid the use of abusive language against any individual or group of individuals.This paper describes the approach of team HARMONY for the “Abusive Comment Detection” shared task at the Third Workshop on Speech and Language Technologies for Dravidian Languages.A Transformer-based ensemble learning approach is proposed for detecting abusive comments in code-mixed (Tamil-English) language and Tamil language. The proposed architecture achieved rank 2 in Tamil text classification sub task and rank 3 in code mixed text classification sub task with macro-F1 score of 0.41 for Tamil and 0.50 for code-mixed data.

In recent years, the growth of online platforms and social media has given rise to a concerning increase in the presence of abusive content. This poses significant challenges for maintaining a safe and inclusive digital environment. In order to resolve this issue, this paper experiments an approach for detecting abusive comments. We are using a combination of pipelining and vectorization techniques, along with algorithms such as the stochastic gradient descent (SGD) classifier and support vector machine (SVM) classifier. We conducted experiments on an Tamil-English code mixed dataset to evaluate the performance of this approach. Using the stochastic gradient descent classifier algorithm, we achieved a weighted F1 score of 0.76 and a macro score of 0.45 for development dataset. Furthermore, by using the support vector machine classifier algorithm, we obtained a weighted F1 score of 0.78 and a macro score of 0.42 for development dataset. With the test dataset, SGD approach secured 5th rank with 0.44 macro F1 score, while SVM scored 8th rank with 0.35 macro F1 score in the shared task. The top rank team secured 0.55 macro F1 score.

This paper presents a study on the language understanding of the Dravidian languages. Three specific tasks related to text classification are focused on in this study, including abusive comment detection, sentiment analysis and fake news detection. The paper provides a detailed description of the tasks, including dataset information and task definitions, as well as the model architectures and training details used to tackle them. Finally, the competition results are presented, demonstrating the effectiveness of the proposed approach for handling these challenging NLP tasks in the context of the Dravidian languages.

Sentiment analysis in code-mixed text written in Dravidian languages. Specifically, Tamil- English and Tulu-English. This paper describes the system paper of the RANLP-2023 shared task. The goal of this shared task is to develop systems that accurately classify the sentiment polarity of code-mixed comments and posts. be provided with development, training, and test data sets containing code-mixed text in Tamil- English and Tulu-English. The task involves message-level polarity classification, to classify YouTube comments into positive, negative, neutral, or mixed emotions. This Code- Mix was compiled by RANLP-2023 organizers from posts on social media. We use classification techniques SVM and achieve an F1 score of 0.147 for Tamil-English and 0.518 for Tulu- English.

With the prevalence of code-mixing among speakers of Dravidian languages, DravidianLangTech proposed the shared task on Sentiment Analysis in Tamil and Tulu at RANLP 2023. This paper presents the submission of LIDOMA, which proposes a methodology that combines lexical features and Convolutional Neural Networks (CNNs) to address the challenge. A fine-tuned 6-layered CNN model is employed, achieving macro F1 scores of 0.542 and 0.199 for Tulu and Tamil, respectively

The paper demonstrates the submission of the team nlpt_malayalm to the Fake News Detection in Dravidian Languages-DravidianLangTech@LT-EDI-2023. The rapid dissemination of fake news and misinformation in today’s digital age poses significant societal challenges. This research paper addresses the issue of fake news detection in the Malayalam language by proposing a novel approach based on the XLM-RoBERTa base model. The objective is to develop an effective classification model that accurately differentiates between genuine and fake news articles in Malayalam. The XLM-RoBERTa base model, known for its multilingual capabilities, is fine-tuned using the prepared dataset to adapt it specifically to the nuances of the Malayalam language. A thorough analysis is also performed to identify any biases or limitations in the model’s performance. The results demonstrate that the proposed model achieves a remarkable macro-averaged F-Score of 87% in the Malayalam fake news dataset, ranking 2nd on the respective task. This indicates its high accuracy and reliability in distinguishing between real and fake news in Malayalam.

Code-mixing presents challenges to sentiment analysis due to limited availability of annotated data found on low-resource languages such as Tulu. To address this issue, comprehensive work was done in creating a gold-standard labeled corpus that incorporates both languages while facilitating accurate analyses of sentiments involved. Encapsulated within this research was the employed use of varied techniques including data collection, cleaning processes as well as preprocessing leading up to effective annotation along with finding results using fine tuning indic bert and performing experiments over tf-idf plus bag of words. The outcome is an invaluable resource for developing custom-tailored models meant solely for analyzing sentiments involved with code mixed texts across Tamil and Tulu domain limits; allowing a focused insight into what makes up such expressions. Remarkably, the adoption of hybrid models yielded promising outcomes, culminating in a 10th rank achievement for Tulu, and a 14thrank achievement for Tamil, supported by an macro F1 score of 0.471 and 0.124 respectively.

The primary focus of this research endeavor lies in detecting and mitigating misinformation within the intricate framework of the Dravidian language. A notable feat was achieved by employing fine-tuning methodologies on the highly acclaimed Indic BERT model, securing a commendable fourth rank in a prestigious competition organized by DravidianLangTech 2023 while attaining a noteworthy macro F1-Score of 0.78. To facilitate this undertaking, a diverse and comprehensive dataset was meticulously gathered from prominent social media platforms, including but not limited to Facebook and Twitter. The overarching objective of this collaborative initiative was to proficiently discern and categorize news articles into either the realm of veracity or deceit through the astute application of advanced machine learning techniques, coupled with the astute exploitation of the distinctive linguistic idiosyncrasies inherent to the Dravidian language.

Fake News Detection in Dravidian Languages is a shared task that identifies youtube comments in the Malayalam language for fake news detection. In this work, we have proposed a transformer-based model with cross-entropy loss and focal loss, which classifies the comments into fake or authentic news. We have used different transformer-based models for the dataset with modifications in the experimental setup, out of which the fine-tuned model, which is based on MuRIL with focal loss, achieved the best overall macro F1-score of 0.87, and we got second position in the final leaderboard.

We participated in the Sentiment Analysis in Tamil and Tulu - DravidianLangTech 2023-RANLP 2023 task in the team name of VEL. This research focuses on addressing the challenge of detecting sentiment analysis in social media code-mixed comments written in Tamil and Tulu languages. Code-mixed text in social media often deviates from strict grammar rules and incorporates non-native scripts, making sentiment identification a complex task. To tackle this issue, we employ pre-processing techniques to remove unnecessary content and develop a model specifically designed for sentiment analysis detection. Additionally, we explore the effectiveness of traditional machine-learning models combined with feature extraction techniques. Our best model logistic regression configurations achieve impressive macro F1 scores of 0.43 on the Tamil test set and 0.51 on the Tulu test set, indicating promising results in accurately detecting instances of sentiment in code-mixed comments.

The use of abusive language on social media platforms is a prevalent issue that requires effective detection. Researchers actively engage in abusive language detection and sentiment analysis on social media platforms. However, most of the studies are in English. Hence, there is a need to develop models for low-resource languages. Further, the multimodal content in social media platforms is expanding rapidly. Our research aims to address this gap by developing a multimodal abusive language detection and performing sentiment analysis for Tamil and Malayalam, two under-resourced languages, based on the shared task Multimodal Abusive Language Detection and Sentiment Analysis in Dravidian Languages: DravidianLangTech@RANLP 2023”. In our study, we conduct extensive experiments utilizing multiple deep-learning models to detect abusive language in Tamil and perform sentiment analysis in Tamil and Malayalam. For feature extraction, we use the mBERT transformer-based model for texts, the ViT model for images and MFCC for audio. In the abusive language detection task, we achieved a weighted average F1 score of 0.5786, securing the first rank in this task. For sentiment analysis, we achieved a weighted average F1 score of 0.357 for Tamil and 0.233 for Malayalam, ranking first in this task.

This paper focuses on using Support Vector Machines (SVM) classifiers with TF-IDF feature extraction to classify whether a comment is abusive or not.The paper tries to identify abusive content in regional languages.The dataset analysis presents the distribution of target variables in the Tamil-English, Telugu-English, and Tamil datasets.The methodology section describes the preprocessing steps, including consistency, removal of special characters and emojis, removal of stop words, and stemming of data. Overall, the study contributes to the field of abusive comment detection in Tamil and Telugu languages.

Abusive comments in online platforms have become a significant concern, necessitating the development of effective detection systems. However, limited work has been done in low resource languages, including Dravidian languages. This paper addresses this gap by focusing on abusive comment detection in a dataset containing Tamil, Tamil-English and Telugu-English code-mixed comments. Our methodology involves logistic regression and explores suitable embeddings to enhance the performance of the detection model. Through rigorous experimentation, we identify the most effective combination of logistic regression and embeddings. The results demonstrate the performance of our proposed model, which contributes to the development of robust abusive comment detection systems in low resource language settings. Keywords: Abusive comment detection, Dravidian languages, logistic regression, embeddings, low resource languages, code-mixed dataset.

This study addresses the challenge of detecting fake news in Dravidian languages by leveraging Google’s MuRIL (Multilingual Representations for Indian Languages) model. Drawing upon previous research, we investigate the intricacies involved in identifying fake news and explore the potential of transformer-based models for linguistic analysis and contextual understanding. Through supervised learning, we fine-tune the “muril-base-cased” variant of MuRIL using a carefully curated dataset of labeled comments and posts in Dravidian languages, enabling the model to discern between original and fake news. During the inference phase, the fine-tuned MuRIL model analyzes new textual content, extracting contextual and semantic features to predict the content’s classification. We evaluate the model’s performance using standard metrics, highlighting the effectiveness of MuRIL in detecting fake news in Dravidian languages and contributing to the establishment of a safer digital ecosystem. Keywords: fake news detection, Dravidian languages, MuRIL, transformer-based models, linguistic analysis, contextual understanding.

This research paper focuses on sentiment analysis of Tamil and Tulu texts using a BERT model and an RNN model. The BERT model, which was pretrained, achieved satisfactory performance for the Tulu language, with a Macro F1 score of 0.352. On the other hand, the RNN model showed good performance for Tamil language sentiment analysis, obtaining a Macro F1 score of 0.208. As future work, the researchers aim to fine-tune the models to further improve their results after the training process.

This research focuses on identifying abusive language in comments. The study utilizes deep learning models, including Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs), to analyze linguistic patterns. Specifically, the LSTM model, a type of RNN, is used to understand the context by capturing long-term dependencies and intricate patterns in the input sequences. The LSTM model achieves better accuracy and is enhanced through the addition of a dropout layer and early stopping. For detecting abusive language in Telugu and Tamil-English, an LSTM model is employed, while in Tamil abusive language detection, a word-level RNN is developed to identify abusive words. These models process text sequentially, considering overall content and capturing contextual dependencies.

We present several models for sentiment analysis of multimodal movie reviews in Tamil and Malayalam into 5 separate classes: highly negative, negative, neutral, positive, and highly positive, based on the shared task, “Multimodal Abusive Language Detection and Sentiment Analysis” at RANLP-2023. We use transformer language models to build text and audio embeddings and then compare the performance of multiple classifier models trained on these embeddings: a Multinomial Naive Bayes baseline, a Logistic Regression, a Random Forest, and an SVM. To account for class imbalance, we use both naive resampling and SMOTE. We found that without resampling, the baseline models have the same performance as a naive Majority Class Classifier. However, with resampling, logistic regression and random forest both demonstrate gains over the baseline.

Sentiment Analysis (SA) is a field of computational study that focuses on analyzing and understanding people’s opinions, attitudes, and emotions towards an entity. An entity could be an individual, an event, a topic, a product etc., which is most likely to be covered by reviews and such reviews can be found in abundance on social media platforms. The increase in the number of social media users and the growing amount of user-generated code-mixed content such as reviews, comments, posts etc., on social media have resulted in a rising demand for efficient tools capable of effectively analyzing such content to detect the sentiments. However, SA of social media text is challenging due to the complex nature of the code-mixed text. To tackle this issue, in this paper, we team MUCS, describe learning models submitted to “Sentiment Analysis in Tamil and Tulu” -DravidianLangTech@Recent Advances In Natural Language Processing (RANLP) 2023. Using fastText embeddings to train the Machine Learning (ML) models to perform SA in code-mixed Tamil and Tulu texts, the proposed methodology exhibited F1 scores of 0.14 and 0.204 securing 13th and 15th rank for Tamil and Tulu texts respectively.

Abusive language detection in user-generated online content has become a pressing concern due to its negative impact on users and challenges for policy makers. Online platforms are faced with the task of moderating abusive content to mitigate societal harm, adhere to legal requirements, and foster inclusivity. Despite numerous methods developed for automated detection of abusive language, the problem continues to persist. This ongoing challenge necessitates further research and development to enhance the effectiveness of abusive content detection systems and implement proactive measures to create safer and more respectful online spaces. To address the automatic detection of abusive languages in social media platforms, this paper describes the models submitted by our team - MUCS to the shared task “Abusive Comment Detection in Tamil and Telugu” at DravidianLangTech - in Recent Advances in Natural Language Processing (RANLP) 2023. This shared task addresses the abusive comment detection in code-mixed Tamil, Telugu, and romanized Tamil (Tamil-English) texts. Two distinct models: i) AbusiveML - a model implemented utilizing Linear Support Vector Classifier (LinearSVC) algorithm fed with n-grams of words and character sequences within word boundary (char_wb) features and ii) AbusiveTL - a Transfer Learning (TL ) model with three different Bidirectional Encoder Representations from Transformers (BERT) models along with random oversampling to deal with data imbalance, are submitted to the shared task for detecting abusive language in the given code-mixed texts. The AbusiveTL model fared well among these two models, with macro F1 scores of 0.46, 0.74, and 0.49 for code-mixed Tamil, Telugu, and Tamil-English texts respectively.

Sentiment Analysis (SA) examines the subjective content of a statement, such as opinions, assessments, feelings, or attitudes towards a subject, person, or a thing. Though several models are developed for SA in high-resource languages like English, Spanish, German, etc., uder-resourced languages like Dravidian languages are less explored. To address the challenges of SA in low resource Dravidian languages, in this paper, we team MUNLP describe the models submitted to “Sentiment Analysis in Tamil and Tulu- DravidianLangTech” shared task at Recent Advances in Natural Language Processing (RANLP)-2023. n-gramsSA, EmbeddingsSA and BERTSA are the models proposed for SA shared task. Among all the models, BERTSA exhibited a maximum macro F1 score of 0.26 for code-mixed Tamil texts securing 2nd place in the shared task. EmbeddingsSA exhibited maximum macro F1 score of 0.53 securing 2nd place for Tulu code-mixed texts.

User-generated social media texts are a blend of resource-rich languages like English and low-resource Dravidian languages like Tamil, Kannada, Tulu, etc. These texts referred to as code-mixing texts are enriching social media since they are written in two or more languages using either a common language script or various language scripts. However, due to the complex nature of the code-mixed text, in this paper, we - team MUCSD, describe a Machine learning (ML) models submitted to “Sentiment Analysis in Tamil and Tulu” shared task at DravidianLangTech@RANLP 2023. The proposed methodology makes use of ML models such as Linear Support Vector Classifier (LinearSVC), LR, and ensemble model (LR, DT, and SVM) to perform SA in Tamil and Tulu languages. The proposed LinearSVC model’s predictions submitted to the shared tasks, obtained 8th and 9th rank for Tamil-English and Tulu-English respectively.

Social media is widely used to spread fake news, which affects a larger population. So it is considered as a very important task to detect fake news spread on social media platforms. To address the challenges in the identification of fake news in the Malayalam language, in this paper, we - team MUCS, describe the Machine Learning (ML) models submitted to “Fake News Detection in Dravidian Languages” at DravidianLangTech@RANLP 2023 shared task. Three different models, namely, Multinomial Naive Bayes (MNB), Logistic Regression (LR), and Ensemble model (MNB, LR, and SVM) are trained using Term Frequency - Inverse Document Frequency (TF-IDF) of word unigrams. Among the three models ensemble model performed better with a macro F1-score of 0.83 and placed 3rd rank in the shared task.

Our work aims to identify the negative comments that is associated with Counter-speech,Xenophobia, Homophobia,Transphobia, Misandry, Misogyny, None-of-the-above categories, In order to identify these categories from the given dataset, we propose three different models such as traditional machine learning techniques, deep learning model and transfer Learning model called BERT is also used to analyze the texts. In the Tamil dataset, we are training the models with Train dataset and test the models with Validation data. Our Team Participated in the shared task organised by DravidianLangTech and secured 4th rank in the task of abusive comment detection in Tamil with a macro- f1 score of 0.35. Also, our run was submitted for abusive comment detection in code-mixed languages (Tamil-English) and secured 6th rank with a macro-f1 score of 0.42.

Sentiment Analysis is a process that involves analyzing digital text to determine the emo- tional tone, such as positive, negative, neu- tral, or unknown. Sentiment Analysis of code- mixed languages presents challenges in natural language processing due to the complexity of code-mixed data, which combines vocabulary and grammar from multiple languages and cre- ates unique structures. The scarcity of anno- tated data and the unstructured nature of code- mixed data are major challenges. To address these challenges, we explored various tech- niques, including Machine Learning models such as Decision Trees, Random Forests, Lo- gistic Regression, and Gaussian Na ̈ıve Bayes, Deep Learning model, such as Long Short- Term Memory (LSTM), and Transfer Learning model like BERT, were also utilized. In this work, we obtained the dataset from the Dravid- ianLangTech shared task by participating in a competition and accessing train, development and test data for Tamil Language. The results demonstrated promising performance in senti- ment analysis of code-mixed text. Among all the models, deep learning model LSTM pro- vides best accuracy of 0.61 for Tamil language.

Code-mixing is a word or phrase-level act of interchanging two or more languages during a conversation or in written text within a sentence. This phenomenon is widespread on social media platforms, and understanding the underlying abusive comments in a code-mixed sentence is a complex challenge. We present our system in our submission for the DravidianLangTech Shared Task on Abusive Comment Detection in Tamil and Telugu. Our approach involves building a multiclass abusive detection model that recognizes 8 different labels. The provided samples are code-mixed Tamil-English text, where Tamil is represented in romanised form. We focused on the Multiclass classification subtask, and we leveraged Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). Our method exhibited its effectiveness in the shared task by earning the ninth rank out of all competing systems for the classification of abusive comments in the code-mixed text. Our proposed classifier achieves an impressive accuracy of 0.99 and an F1-score of 0.99 for a balanced dataset using TF-IDF with SVM. It can be used effectively to detect abusive comments in Tamil, English code-mixed text

With 102,530,067 items currently in its crowd-sourced knowledge base, Wikidata provides NLP practitioners a unique and powerful resource for inference and reasoning over real-world entities. However, because Wikidata is very entity focused, events and actions are often labeled with eventive nouns (e.g., the process of diagnosing a person’s illness is labeled “diagnosis”), and the typical participants in an event are not described or linked to that event concept (e.g., the medical professional or patient). Motivated by a need for an adaptable, comprehensive, domain-flexible ontology for information extraction, including identifying the roles entities are playing in an event, we present a curated subset of Wikidata in which events have been enriched with PropBank roles. To enable richer narrative understanding between events from Wikidata concepts, we have also provided a comprehensive mapping from temporal Qnodes and Pnodes to the Allen Interval Temporal Logic relations.

The paper presents the work on the selection, semantic annotation and classification of a group of verbs from WordNet, characterized with the semantic primitive ‘verbs of contact’ that belong to the common Bulgarian lexis. The selection of the verb set using both different criteria: statistical information from corpora, WordNet Base concepts and AoA as a criterion, is described. The focus of the work is on the process of the verbs’ of contact semantic annotation using the combined information from two language resources - WordNet and FrameNet. The verbs of contact from WordNet are assigmed semantic frames from FrameNet and then grouped in semantic subclasses using both their place in the WordNet hierarchy, the semantic restrictions on their frame elements and the corresponding syntactic realization. At the end we offer some conclusions on the classification of ‘verbs of contact’ in semantic subtypes.

In this work, we address the annotation of language resources through the application of the engagement network in appraisal theory. This work represents an attempt to extend the advances in studies of speech and dialogue acts to encompass the latest notion of stance negotiations in discourse, between the writer and other sources. This type of phenomenon has become especially salient in contemporary media communication and requires some timely research to address emergent requirement. We shall first of all describe the engagement network as proposed by Martin and White (2005) and then discuss the issue of multisubjectivity. We shall then propose and describe a bi-step procedure towards better annotation before discussing the benefits of engagement network in the assessment of speaker-writer stance. We shall finally discuss issues of annotation consistency and reliability.

This paper presents a graphical editor for directed graphs, serialised in the PENMAN format, as used for annotations in Abstract Meaning Representation (AMR). The tool supports creating and modifying of AMR graphs and other directed graphs, adding and deletion of instances, edges and literals, renaming of concepts, relations and literals, setting a “top node” and validating the edited graph.

Personal nouns, i.e. common nouns denoting human beings, play an important role in manifesting gender and gender stereotypes in texts, especially for languages with grammatical gender like German. Automatically detecting and extracting personal nouns can thus be of interest to a myriad of different tasks such as minimizing gender bias in language models and researching gender stereotypes or gender-fair language, but is complicated by the morphological heterogeneity and homonymy of personal and non-personal nouns, which restrict lexicon-based approaches. In this paper, we introduce a classifier created by fine-tuning a transformer model that detects personal nouns in German. Although some phenomena like homonymy and metalinguistic uses are still problematic, the model is able to classify personal nouns with robust accuracy (f1-score: 0.94).

The article discusses the challenges of cross-linguistic dialogue act annotation, which involves using methods developed for one language to annotate conversations in another language. The article specifically focuses on the research on dialogue act annotation in Polish, based on the ISO standard developed for English. The article examines the differences between Polish and English in dialogue act annotation based on selected examples from DiaBiz.Kom corpus, such as the use of honorifics in Polish, the use of inflection to convey meaning in Polish, the tendency to use complex sentence structures in Polish, and the cultural differences that may play a role in the annotation of dialogue acts. The article also discusses the creation of DiaBiz.Kom, a Polish dialogue corpus based on ISO 24617-2 standard applied to 1100 transcripts.

Using recent developments in count noun quantification, namely Referential Transparency Theory (RTT), the basic structure for annotating quantification in the nominal domain according to RTT is presented. The paper discusses core ideas of RTT, derives the abstract annotation syntax, and exemplifies annotations of quantified noun phrases partly in comparison to QuantML.

This paper discusses some issues in the semantic annotation of quantification phenomena in general, and in particular in the markup language QuantML, which has been proposed to form part of an ISO standard annotation scheme for quantification in natural language data. QuantML annotations have been claimed to have a compositional semantic interpretation, but the formal specification of QuantML in the official ISO documentation does not provide sufficient detail to judge this. This paper aims to fill this gap.

VoxML is a modeling language used to map natural language expressions into real time visualizations using real-world semantic knowledge of objects and events. Its utility has been demonstrated in embodied simulation environmens and in agent-object interactions in situated human-agent communicative. It is enriched to work with notions of affordances, both Gibsonian and Telic, and habitat for various interactions between the rational agent (human) and an object. This paper aims to specify VoxML as an annotation language in general abstract terms. It then shows how it works on annotating linguistic data that express visually perceptible human-object interactions. The annotation structures thus generated will be interpreted against the enriched minimal model created by VoxML as a modeling language while supporting the modeling purposes of VoxML linguistically.

In this work, we assess the quality of different utterance segmentation techniques as an aid in annotating collaborative problem solving in teams and the creation of shared meaning between participants in a situated, collaborative task. We manually transcribe utterances in a dataset of triads collaboratively solving a problem involving dialogue and physical object manipulation, annotate collaborative moves according to these gold-standard transcripts, and then apply these annotations to utterances that have been automatically segmented using toolkits from Google and Open-AI’s Whisper. We show that the oracle utterances have minimal correspondence to automatically segmented speech, and that automatically segmented speech using different segmentation methods is also inconsistent. We also show that annotating automatically segmented speech has distinct implications compared with annotating oracle utterances — since most annotation schemes are designed for oracle cases, when annotating automatically-segmented utterances, annotators must make arbitrary judgements which other annotators may not replicate. We conclude with a discussion of how future annotation specs can account for these needs.

Natural Language Processing (NLP) tools have been rapidly adopted in political science for the study of conflict and violence. In this paper, we present an application to analyze various lethal and non-lethal events conducted by organized criminal groups and state forces in Mexico. Based on a large corpus of news articles in Spanish and a set of high-quality annotations, the application evaluates different Machine Learning (ML) algorithms and Large Language Models (LLMs) to classify documents and individual sentences, and to identify specific behaviors related to organized criminal violence and law enforcement efforts. Our experiments support the growing evidence that BERT-like models achieve outstanding classification performance for the study of organized crime. This application amplifies the capacity of conflict scholars to provide valuable information related to important security challenges in the developing world.

The method method presented in this paper uses a BERT model for classifying location mentions in event reporting news texts into two classes: a place of an event, called main location, or another location mention, called here secondary location. Our evaluation on articles, reporting protests, shows promising results and demonstrates the feasibility of our approach and the event geolocation task in general. We evaluate our method against a simple baseline and state of the art ML models and we achieve a significant improvement in all cases by using the BERT model. In contrast to other location classification approaches, we completelly avoid lingusitic pre processing and feature engineering, which is a pre-requisite for all multi-domain and multilingual applications.

Social media has become an established platform for people to organize and take offline actions, often in the form of civil unrest. Understanding these events can help support pro-democratic movements. The primary method to detect these events on Twitter relies on aggregating many tweets, but this includes many that are not relevant to the task. We propose a multi-instance learning (MIL) approach, which jointly identifies relevant tweets and detects civil unrest events. We demonstrate that MIL improves civil unrest detection over methods based on simple aggregation. Our best model achieves a 0.73 F1 on the Global Civil Unrest on Twitter (G-CUT) dataset.

Identifying cause-effect relations plays an integral role in the understanding and interpretation of natural languages. Furthermore, automated mining of causal relations from news and text about socio-political events is a stepping stone in gaining critical insights, including analyzing the scale, frequency and trends across timelines of events, as well as anticipating future ones. The Shared Task 3, part of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE @ RANLP 2023), involved the task of Event Causality Identification with Causal News Corpus. We describe our approach to Subtask 1, dealing with causal event classification, a supervised binary classification problem to annotate given event sentences with whether they contained any cause-effect relations. To help achieve this task, a BERT based architecture - RoBERTa was implemented. The results of this model are validated on the dataset provided by the organizers of this task.

Understanding causality is a core aspect of intelligence. The Event Causality Identification with Causal News Corpus Shared Task addresses two aspects of this challenge: Subtask 1 aims at detecting causal relationships in texts, and Subtask 2 requires identifying signal words and the spans that refer to the cause or effect, respectively. Our system, which is based on pre-trained transformers, stacked sequence tagging, and synthetic data augmentation, ranks third in Subtask 1 and wins Subtask 2 with an F1 score of 72.8, corresponding to a margin of 13 pp. to the second-best system.

Mapping ongoing news headlines to event-related classes in a rich knowledge base can be an important component in a knowledge-based event analysis and forecasting solution. In this paper, we present a methodology for creating a benchmark dataset of news headlines mapped to event classes in Wikidata, and resources for the evaluation of methods that perform the mapping. We use the dataset to study two classes of unsupervised methods for this task: 1) adaptations of classic entity linking methods, and 2) methods that treat the problem as a zero-shot text classification problem. For the first approach, we evaluate off-the-shelf entity linking systems. For the second approach, we explore a) pre-trained natural language inference (NLI) models, and b) pre-trained large generative language models. We present the results of our evaluation, lessons learned, and directions for future work. The dataset and scripts for evaluation are made publicly available.

Hate speech detection during times of war has become crucial in recent years, as evident with the recent Russo-Ukrainian war. In this paper, we present our submissions for both subtasks from the Multimodal Hate Speech Event Detec- tion contest at CASE 2023, RANLP 2023. We used pre-trained BERT models in both submis- sion, achieving a F1 score of 0.809 in subtask A, and F1 score of 0.567 in subtask B. In the first subtask, our result was not far from the first place, which led us to realize the lower impact of images in real-life memes about feel- ings, when compared with the impact of text. However, we observed a higher importance of images when targeting hateful feelings towards a specific entity. The source code to reproduce our results can be found at the github repository https://github.com/JesusASmx/OmeteotlAtCASE2023

Causal events play a crucial role in explaining the intricate relationships between the causes and effects of events. However, comprehending causal events within discourse, text, or speech poses significant semantic challenges. We propose a contrastive learning-based method in this submission to the Causal News Corpus - Event Causality Shared Task 2023, with a specific focus on SubTask1 centered on causal event classification. In our approach we pre-train our base model using Supervised Contrastive (SuperCon) learning. Subsequently, we fine-tune the pre-trained model for the specific task of causal event classification. Our experimentation demonstrates the effectiveness of our method, achieving a competitive performance, and securing the 2nd position on the leaderboard with an F1-Score of 84.36.

In this research paper, we propose a multimodal approach to hate speech detection, directed towards the identification of hate speech and its related targets. Our method uses logistic regression and support vector machines (SVMs) to analyse textual content extracted from social media platforms. We exploit natural language processing techniques to preprocess and extract relevant features from textual content, capturing linguistic patterns, sentiment, and contextual information.

Text-embedded images can serve as a means of spreading hate speech, propaganda, and extremist beliefs. Throughout the Russia-Ukraine war, both opposing factions heavily relied on text-embedded images as a vehicle for spreading propaganda and hate speech. Ensuring the effective detection of hate speech and propaganda is of utmost importance to mitigate the negative effect of hate speech dissemination. In this paper, we outline our methodologies for two subtasks of Multimodal Hate Speech Event Detection 2023. For the first subtask, hate speech detection, we utilize multimodal deep learning models boosted by ensemble learning and syntactical text attributes. For the second subtask, target detection, we employ multimodal deep learning models boosted by named entity features. Through experimentation, we demonstrate the superior performance of our models compared to all textual, visual, and text-visual baselines employed in multimodal hate speech detection. Furthermore, our models achieve the first place in both subtasks on the final leaderboard of the shared task.

Hate speech detection has emerged as a critical research area in recent years due to the rise of online social platforms and the proliferation of harmful content targeting individuals or specific groups.This task highlights the importance of detecting hate speech in text-embedded images.By leveraging deep learning models,this research aims to uncover the connection between hate speech and the entities it targets.

With a surge in the usage of social media postings to express opinions, emotions, and ideologies, there has been a significant shift towards the calibration of social media as a rapid medium of conveying viewpoints and outlooks over the globe. Concurrently, the emergence of a multitude of conflicts between two entities has given rise to a stream of social media content containing propaganda, hate speech, and inconsiderate views. Thus, the issue of monitoring social media postings is rising swiftly, attracting major attention from those willing to solve such problems. One such problem is Hate Speech detection. To mitigate this problem, we present our novel ensemble learning approach for detecting hate speech, by classifying text-embedded images into two labels, namely “Hate Speech” and “No Hate Speech” . We have incorporated state-of-art models including InceptionV3, BERT, and XLNet. Our proposed ensemble model yielded promising results with 75.21 and 74.96 as accuracy and F-1 score (respectively). We also present an empirical evaluation of the text-embedded images to elaborate on how well the model was able to predict and classify.

The paper presents a semantic model of protest events, called Semantic Interpretations of Protest Events (SemInPE). The analytical framework used for building the semantic representations is inspired by the object-oriented paradigm in computer science and a cognitive approach to the linguistic analysis. The model is a practical application of the Unified Eventity Representation (UER) formalism, which is based on the Unified Modeling Language (UML). The multi-layered architecture of the model provides flexible means for building the semantic representations of the language objects along a scale of generality and specificity. Thus, it is a suitable environment for creating the elements of ontologies on various topics and for different languages.

The emergence of social media and e-commerce platforms enabled the perpetrator to spread negativity and abuse individuals or organisations worldwide rapidly. It is critical to detect hate speech in both visual and textual content so that it may be moderated or excluded from online platforms to keep it sound and safe for users. However, multimodal hate speech detection is a complex and challenging task as people sarcastically present hate speech and different modalities i.e., image and text are involved in their content. This paper describes our participation in the CASE 2023 multimodal hate speech event detection task. In this task, the objective is to automatically detect hate speech and its target from the given text-embedded image. We proposed a transformer-based multimodal hierarchical fusion model to detect hate speech present in the visual content. We jointly fine-tune a language and a vision pre-trained transformer models to extract the visual-contextualized features representation of the text-embedded image. We concatenate these features and fed them to the multi-sample dropout strategy. Moreover, the contextual feature vector is fed into the BiLSTM module and the output of the BiLSTM module also passes into the multi-sample dropout. We employed arithmetic mean fusion to fuse all sample dropout outputs that predict the final label of our proposed method. Experimental results demonstrate that our model obtains competitive performance and ranked 5th among the participants

Cause-effect relationships play a crucial role in human cognition, and distilling cause-effect relations from text helps in ameliorating causal networks for predictive tasks. There are many NLP applications that can benefit from this task, including natural language-based financial forecasting, text summarization, and question-answering. However, due to the lack of syntactic clues, the ambivalent semantic meaning of words, complex sentence structure, and implicit meaning of numerical entities in the text make it one of the challenging tasks in NLP. To address these challenges, CASE-2023 introduced a shared task 3 task focusing on event causality identification with causal news corpus. In this paper, we demonstrate our participant systems for this task. We leverage two transformers models including DeBERTa and Twitter-RoBERTa along with the weighted average fusion technique to tackle the challenges of subtask 1 where we need to identify whether a text belongs to either causal or not. For subtask 2 where we need to identify the cause, effect, and signal tokens from the text, we proposed a unified neural network of DeBERTa and DistilRoBERTa transformer variants with contrastive learning techniques. The experimental results showed that our proposed method achieved competitive performance among the participants’ systems.

Event extraction from textual data is a NLP research task relevant to a plethora of domains. Most approaches aim to recognize events from a predefined event schema, consisting of event types and their corresponding arguments. For domains, such as disinformation, where new event types emerge frequently, there is a need to adapt such fixed event schemas to accommodate for new event types. We present NEXT (New Event eXTraction) - a resource-sparse approach to extending a close-domain model to novel event types, that requires a very small number of annotated samples for fine-tuning performed on a single GPU. Furthermore, our results suggest that this approach is suitable not only for extraction of new event types, but also for recognition of existing event types, as the use of this approach on a new dataset leads to improved recall for all existing events while retaining precision.

The scarcity of data poses a significant challenge in closed-domain event extraction, as is common in complex NLP tasks. This limitation primarily arises from the intricate nature of the annotation process. To address this issue, we present a multi-task model structure and training approach that leverages the additional data, which is found as not having any event information at document and sentence levels, generated during the event annotation process. By incorporating this supplementary data, our proposed framework demonstrates enhanced robustness and, in some scenarios, improved performance. A particularly noteworthy observation is that including only negative documents in addition to the original data contributes to performance enhancement. Our findings offer promising insights into leveraging extra data to mitigate data scarcity challenges in closed-domain event extraction.

Hate speech has emerged as a pressing issue on social media platforms, fueled by the increasing availability of multimodal data and easy internet access. Addressing this problem requires collaborative efforts from researchers, policymakers, and online platforms. In this study, we investigate the detection of hate speech in multimodal data, comprising text-embedded images, by employing advanced deep learning models. The main objective is to identify effective strategies for hate speech detection and content moderation. We conducted experiments using four state-of-the-art classifiers: XLM-Roberta-base, BiLSTM, XLNet base cased, and ALBERT, on the CrisisHateMM[4] dataset, consisting of over 4700 text-embedded images related to the Russia-Ukraine conflict. The best findings reveal that XLM-Roberta-base exhibits superior performance, outperforming other classifiers across all evaluation metrics, including an impressive F1 score of 84.62 for sub-task 1 and 69.73 for sub-task 2. The future scope of this study lies in exploring multimodal approaches to enhance hate speech detection accuracy, integrating ethical considerations to address potential biases, promoting fairness, and safeguarding user rights. Additionally, leveraging larger and more diverse datasets will contribute to developing more robust and generalised hate speech detection solutions.

The Event Causality Identification Shared Task of CASE 2023 is the second iteration of a shared task centered around the Causal News Corpus. Two subtasks were involved: In Subtask 1, participants were challenged to predict if a sentence contains a causal relation or not. In Subtask 2, participants were challenged to identify the Cause, Effect, and Signal spans given an input causal sentence. For both subtasks, participants uploaded their predictions for a held-out test set, and ranking was done based on binary F1 and macro F1 scores for Subtask 1 and 2, respectively. This paper includes an overview of the work of the ten teams that submitted their results to our competition and the six system description papers that were received. The highest F1 scores achieved for Subtask 1 and 2 were 84.66% and 72.79%, respectively.

Ensuring the moderation of hate speech and its targets emerges as a critical imperative within contemporary digital discourse. To facilitate this imperative, the shared task Multimodal Hate Speech Event Detection was organized in the sixth CASE workshop co-located at RANLP 2023. The shared task has two subtasks. The sub-task A required participants to pose hate speech detection as a binary problem i.e. they had to detect if the given text-embedded image had hate or not. Similarly, sub-task B required participants to identify the targets of the hate speech namely individual, community, and organization targets in text-embedded images. For both sub-tasks, the participants were ranked on the basis of the F1-score. The best F1-score in sub-task A and sub-task B were 85.65 and 76.34 respectively. This paper provides a comprehensive overview of the performance of 13 teams that submitted the results in Subtask A and 10 teams in Subtask B.

The purpose of the shared task 2 at the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) 2023 workshop was to test the abilities of the participating models and systems to detect and geocode armed conflicts events in social media messages from Telegram channels reporting on the Russo Ukrainian war. The evaluation followed an approach which was introduced in CASE 2021 (Giorgi et al., 2021): For each system we consider the correlation of the spatio-temporal distribution of its detected events and the events identified for the same period in the ACLED (Armed Conflict Location and Event Data Project) database (Raleigh et al., 2010). We use ACLED for the ground truth, since it is a well established standard in the field of event extraction and political trend analysis, which relies on human annotators for the encoding of security events using a fine grained taxonomy. Two systems participated in this shared task, we report in this paper on both the shared task and the participating systems.

We provide a summary of the sixth edition of the CASE workshop that is held in the scope of RANLP 2023. The workshop consists of regular papers, three keynotes, working papers of shared task participants, and shared task overview papers. This workshop series has been bringing together all aspects of event information collection across technical and social science fields. In addition to contributing to the progress in text based event extraction, the workshop provides a space for the organization of a multimodal event information collection task.

We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible. We present our results and findings, which include that just 13% of papers had (i) sufficiently low barriers to reproduction, and (ii) enough obtainable information, to be considered for reproduction, and that all but one of the experiments we selected for reproduction was discovered to have flaws that made the meaningfulness of conducting a reproduction questionable. As a result, we had to change our coordinated study design from a reproduce approach to a standardise-then-reproduce-twice approach. Our overall (negative) finding that the great majority of human evaluations in NLP is not repeatable and/or not reproducible and/or too flawed to justify reproduction, paints a dire picture, but presents an opportunity for a rethink about how to design and report human evaluations in NLP.

Embedding representations of text are useful for downstream natural language processing tasks. Several universal sentence representation methods have been proposed with a particular focus on self-supervised pre-training approaches to leverage the vast quantities of unlabelled data. However, there are two challenges for generating rich embedding representations for a new document. 1) The latest rich embedding generators are based on very large costly transformer-based architectures. 2) The rich embedding representation of a new document is limited to only the information provided without access to any explicit contextual and temporal information that could potentially further enrich the representation. We propose efficient retrieval-augmented text embeddings (ERATE) that tackles the first issue and offers a method to tackle the second issue. To the best of our knowledge, we are the first to incorporate retrieval to general purpose embeddings as a new paradigm, which we apply to the semantic similarity tasks of SentEval. Despite not reaching state-of-the-art performance, ERATE offers key insights that encourages future work into investigating the potential of retrieval-based embeddings.

Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a large number of domain experts for their creation. Thus, it is vital to ensure high-quality domain-specific training data. In this paper, we propose a framework for enhancing the data quality of original datasets. (Code and dataset are available at https://github.com/IvaBojic/framework). We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33%/40% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality.

Context-aware translation can be achieved by processing a concatenation of consecutive sentences with the standard Transformer architecture. This paper investigates the intuitive idea of providing the model with explicit information about the position of the sentences contained in the concatenation window. We compare various methods to encode sentence positions into token representations, including novel methods. Our results show that the Transformer benefits from certain sentence position encoding methods on English to Russian translation, if trained with a context-discounted loss. However, the same benefits are not observed on English to German. Further empirical efforts are necessary to define the conditions under which the proposed approach is beneficial.

Pretrained language models (PLMs) on domain-specific data have been proven to be effective for in-domain natural language processing (NLP) tasks. Our work aimed to develop a language model which can be effective for the NLP tasks with the data from diverse social media platforms. We pretrained a language model on Twitter and Reddit posts in English consisting of 929M sequence blocks for 112K steps. We benchmarked our model and 3 transformer-based models—BERT, BERTweet, and RoBERTa on 40 social media text classification tasks. The results showed that although our model did not perform the best on all of the tasks, it outperformed the baseline model—BERT on most of the tasks, which illustrates the effectiveness of our model. Also, our work provides some insights of how to improve the efficiency of training PLMs.

We introduce two simple randomized variants of byte pair encoding (BPE) and explore whether randomizing the selection of merge operations substantially affects a downstream machine translation task. We focus on translation into morphologically rich languages, hypothesizing that this task may show sensitivity to the method of choosing subwords. Analysis using a Bayesian linear model indicates that one variant performs nearly indistinguishably compared to standard BPE while the other degrades performance less than we anticipated. We conclude that although standard BPE is widely used, there exists an interesting universe of potential variations on it worth investigating. Our code is available at: https://github.com/bltlab/random-bpe.

In recent years, there has been growing interest in the field of abstractive text summarization with focused contributions in relevant model architectures, datasets, and evaluation metrics. Despite notable research advances, previous works have identified certain limitations concerning the quality of datasets and the effectiveness of evaluation techniques for generated summaries. In this context, we examine these limitations further with the help of three quality measures, namely, Information Coverage, Entity Hallucination, and Summarization Complexity. As a part of this work, we investigate two widely used datasets (XSUM and CNNDM) and three existing models (BART, PEGASUS, and BRIO) and report our findings. Some key insights are: 1) Cumulative ROUGE score is an inappropriate evaluation measure since few high-scoring samples dominate the overall performance, 2) Existing summarization models have limited capability for information coverage and hallucinate to generate factual information, and 3) Compared to the model generated summaries, the reference summaries have lowest information coverage and highest entity hallucinations reiterating the need of new and better reference summaries.

The number of scientific publications in the biomedical domain is continuously increasing with time. An efficient system for indexing these publications is required to make the information accessible according to the user’s information needs. Task 10a of the BioASQ challenge aims to classify PubMed articles according to the MeSH ontology so that new publications can be grouped with similar preexisting publications in the field without the assistance of time-consuming and costly annotations by human annotators. In this work, we use Graph Neural Network (GNN) in the link prediction setting to exploit potential graph-structured information present in the dataset which could otherwise be neglected by transformer-based models. Additionally, we provide error analysis and a plausible reason for the substandard performance achieved by GNN.

In this paper, we describe how we unearthed some fundamental problems while building an analogy dataset modelled on BATS (Gladkova et al., 2016) to evaluate historical Irish embeddings on their ability to detect orthographic, morphological and semantic similarity.performance of our models in the analogy task was extremely poor regardless of the architecture, hyperparameters and evaluation metrics, while the qualitative evaluation revealed positive tendencies. argue that low agreement between field experts on fundamental lexical and orthographic issues, and the lack of a unified editorial standard in available resources make it impossible to build reliable evaluation datasets for computational models and obtain interpretable results. We emphasise the need for such a standard, particularly for NLP applications, and prompt Celticists and historical linguists to engage in further discussion. We would also like to draw NLP scholars’ attention to the role of data and its (extra)linguistic properties in testing new models, technologies and evaluation scenarios.

Recent research has demonstrated impressive generalization capabilities of several Knowledge Base Question Answering (KBQA) models on the GrailQA dataset. We inspect whether these models can generalize to other datasets in a zero-shot setting. We notice a significant drop in performance and investigate the causes for the same. We observe that the models are dependent not only on the structural complexity of the questions, but also on the linguistic styles of framing a question. Specifically, the linguistic dimensions corresponding to explicitness, readability, coherence, and grammaticality have a significant impact on the performance of state-of-the-art KBQA models. Overall our results showcase the brittleness of such models and the need for creating generalizable systems.

Active learning has been widely used in the task of text classification for its ability to select the most valuable samples to annotate while improving the model performance. However, the efficiency of active learning in multi-label text classification tasks has been under-explored due to the label imbalanceness problem. In this paper, we conduct an empirical study of active learning on multi-label text classification and evaluate the efficiency of five active learning strategies on six multi-label text classification tasks. The experiments show that some strategies in the single-label setting especially in imbalanced datasets.

We probe structural and discourse aspects of coreferential relationships in a fine-tuned Dutch BERT event coreference model. Previous research has suggested that no such knowledge is encoded in BERT-based models and the classification of coreferential relationships ultimately rests on outward lexical similarity. While we show that BERT can encode a (very) limited number of these discourse aspects (thus disproving assumptions in earlier research), we also note that knowledge of many structural features of coreferential relationships is absent from the encodings generated by the fine-tuned BERT model.

Despite recent successes in language models, their ability to represent numbers is insufficient. Humans conceptualize numbers based on their magnitudes, effectively projecting them on a number line; whereas subword tokenization fails to explicitly capture magnitude by splitting numbers into arbitrary chunks. To alleviate this shortcoming, alternative approaches have been proposed that modify numbers at various stages of the language modeling pipeline. These methods change either the (1) notation in which numbers are written (eg scientific vs decimal), the (2) vocabulary used to represent numbers or the entire (3) architecture of the underlying language model, to directly regress to a desired number. Previous work suggests that architectural change helps achieve state-of-the-art on number estimation but we find an insightful ablation - changing the model”s vocabulary instead (eg introduce a new token for numbers in range 10-100) is a far better trade-off. In the context of masked number prediction, a carefully designed tokenization scheme is both the simplest to implement and sufficient, ie with similar performance to the state-of-the-art approach that requires making significant architectural changes. Finally, we report similar trends on the downstream task of numerical fact estimation (for Fermi Problems) and discuss reasons behind our findings.

Indigenous language expertise is not encoded in written text in the same way as it is for languages that have a long literal tradition. In many cases it is, on the contrary, mostly conserved orally. Therefore the evaluation of neural MT systems solely based on an algorithm learning from written texts is not adequate to measure the quality of a system that is used by the language community. If extensively using tools based on a big amount of non-native language this can even contribute to language change in a way that is not desired by the language community. It can also pollute the internet with automatically created texts that outweigh native texts. We propose a manual evaluation method focusing on flow and content separately, and additionally we use existing rule-based NLP to evaluate other factors such as spelling, grammar and grammatical richness. Our main conclusion is that language expertise of a native speaker is necessary to properly evaluate a given system. We test the method by manually evaluating two neural MT tools for an indigenous low resource language. We present an experiment on two different neural translations to and from North Sámi, an indigenous language of North Europe.

Human evaluation plays a crucial role in Natural Language Processing (NLP) as it assesses the quality and relevance of developed systems, thereby facilitating their enhancement. However, the absence of widely accepted human evaluation metrics in NLP hampers fair comparisons among different systems and the establishment of universal assessment standards. Through an extensive analysis of existing literature on human evaluation metrics, we identified several gaps in NLP evaluation methodologies. These gaps served as motivation for developing our own hierarchical evaluation framework. The proposed framework offers notable advantages, particularly in providing a more comprehensive representation of the NLP system’s performance. We applied this framework to evaluate the developed Machine Reading Comprehension system, which was utilized within a human-AI symbiosis model. The results highlighted the associations between the quality of inputs and outputs, underscoring the necessity to evaluate both components rather than solely focusing on outputs. In future work, we will investigate the potential time-saving benefits of our proposed framework for evaluators assessing NLP systems.

In both the translation industry and translation education, analytic and systematic assessment of translations plays a vital role. However, due to lack of a scheme for describing differences between translations, such assessment has been realized only in an ad-hoc manner. There is prior work on a scheme for describing differences between translations, but it has coverage and objectivity issues. To alleviate these issues and realize more fine-grained analyses, we developed an improved scheme by referring to diverse types of translations and adopting hierarchical linguistic units for analysis, taking English-to-Japanese translation as an example.

This paper presents an overview of, and the results from, the 2023 Shared Task on Reproducibility of Evaluations in NLP (ReproNLP’23), following on from two previous shared tasks on reproducibility of evaluations in NLG, ReproGen’21 and ReproGen’22. This shared task series forms part of an ongoing research programme designed to develop theory and practice of reproducibility assessment in NLP and machine learning, all against a background of an interest in reproducibility that con- tinues to grow in the two fields. This paper describes the ReproNLP’23 shared task, summarises results from the reproduction studies submitted, and provides comparative analysis of the results.

This paper presents a human evaluation reproduction study regarding the data-to-text generation task. The evaluation focuses in counting the supported and contradicting facts generated by a neural data-to-text model with a macro planning stage. The model is tested generating sport summaries for the ROTOWIRE dataset. We first describe the approach to reproduction that is agreed in the context of the ReproHum project. Then, we detail the entire configuration of the original human evaluation and the adaptations that had to be made to reproduce such an evaluation. Finally, we compare the reproduction results with those reported in the paper that was taken as reference.

Human evaluation is crucial for NLG systems as it provides a reliable assessment of the quality, effectiveness, and utility of generated language outputs. However, concerns about the reproducibility of such evaluations have emerged, casting doubt on the reliability and generalisability of reported results. In this paper, we present the findings of a reproducibility study on a data-to-text system, conducted under two conditions: (1) replicating the original setup as closely as possible with evaluators from AMT, and (2) replicating the original human evaluation but this time, utilising evaluators with a background in academia. Our experiments show that there is a loss of statistical significance between the original and reproduction studies, i.e. the human evaluation results are not reproducible. In addition, we found that employing local participants led to more robust results. We finally discuss lessons learned, addressing the challenges and best practices for ensuring reproducibility in NLG human evaluations.

This paper is part of the larger ReproHum project, where different teams of researchers aim to reproduce published experiments from the NLP literature. Specifically, ReproHum focuses on the reproducibility of human evaluation studies, where participants indicate the quality of different outputs of Natural Language Generation (NLG) systems. This is necessary because without reproduction studies, we do not know how reliable earlier results are. This paper aims to reproduce the second human evaluation study of Puduppully & Lapata (2021), while another lab is attempting to do the same. This experiment uses best-worst scaling to determine the relative performance of different NLG systems. We found that the worst performing system in the original study is now in fact the best performing system across the board. This means that we cannot fully reproduce the original results. We also carry out alternative analyses of the data, and discuss how our results may be combined with the other reproduction study that is carried out in parallel with this paper.

This paper presents a partial reproduction study of Data-to-text Generation with Macro Planning by Puduppully et al. (2021). This work was conducted as part of the ReproHum project, a multi-lab effort to reproduce the results of NLP papers incorporating human evaluations. We follow the same instructions provided by the authors and the ReproHum team to the best of our abilities. We collect preference ratings for the following evaluation criteria in order: conciseness, coherence, and grammaticality. Our results are highly correlated with the original experiment. Nonetheless, we believe the presented results are insufficent to conclude that the Macro system proposed and developed by the original paper is superior compared to other systems. We suspect combining our results with the three other reproductions of this paper through the ReproHum project will paint a clearer picture. Overall, we hope that our work is a step towards a more transparent and reproducible research landscape.

There is a growing concern regarding the reproducibility of human evaluation studies in NLP. As part of the ReproHum campaign, we conducted a study to assess the reproducibility of a recent human evaluation study in NLP. Specifically, we attempted to reproduce a human evaluation of a novel approach to enhance Role-Oriented Dialogue Summarization by considering the influence of role interactions. Despite our best efforts to adhere to the reported setup, we were unable to reproduce the statistical results as presented in the original paper. While no contradictory evidence was found, our study raises questions about the validity of the reported statistical significance results, and/or the comprehensiveness with which the original study was reported. In this paper, we provide a comprehensive account of our reproduction study, detailing the methodologies employed, data collection, and analysis procedures. We discuss the implications of our findings for the broader issue of reproducibility in NLP research. Our findings serve as a cautionary reminder of the challenges in conducting reproducible human evaluations and prompt further discussions within the NLP community.

This paper reports a reproduction study of the human evaluation of role-oriented dialogue summarization models, as part of the ReproNLP Shared Task 2023 on Reproducibility of Evaluations in NLP. We outline the disparities between the original study’s experimental design and our reproduction study, along with the outcomes obtained. The inter-annotator agreement within the reproduction study is observed to be lower, measuring 0.40 as compared to the original study’s 0.48. Among the six conclusions drawn in the original study, four are validated in our reproduction study. We confirm the effectiveness of the proposed approach on the overall metric, albeit with slightly poorer relative performance compared to the original study. Furthermore, we raise an open-ended inquiry: how can subjective practices in the original study be identified and addressed when conducting reproduction studies?

How reliable are human evaluation results? Is it possible to replicate human evaluation? This work takes a closer look at the evaluation of the output of a Text-to-Speech (TTS) system. Unfortunately, our results indicate that human evaluation is not as straightforward to replicate as expected. Additionally, we also present results on reproducing the technical background of the TTS system and discuss potential reasons for the reproduction failure.

This paper describes the reproduction of a human evaluation in Language-Agnostic Meta- Learning for Low-Resource Text-to-Speech with Articulatory Features reported in Lux and Vu (2022). It is a contribution to the ReproNLP 2023 Shared Task on Reproducibility of Evaluations in NLP. The original evaluation assessed the naturalness of audio generated by different Text-to-Speech (TTS) systems for German, and our goal was to repeat the experiment with a different set of evaluators. We reproduced the evaluation based on data and instructions provided by the original authors, with some uncertainty concerning the randomisation of question order. Evaluators were recruited via email to relevant mailing lists and we received 157 responses over the course of three weeks. Our initial results show low reproducibility, but when we assume that the systems of the original and repeat evaluation experiment have been transposed, the reproducibility assessment improves markedly. We do not know if and at what point such a transposition happened; however, an initial analysis of our audio and video files provides some evidence that the system assignment in our repeat experiment is correct.

This work presents our efforts to reproduce the results of the human evaluation experiment presented in the paper of Vamvas and Sennrich (2022), which evaluated an automatic system detecting over- and undertranslations (translations containing more or less information than the original) in machine translation (MT) outputs. Despite the high quality of the documentation and code provided by the authors, we discuss some problems we found in reproducing the exact experimental setup and offer recommendations for improving reproducibility. Our replicated results generally confirm the conclusions of the original study, but in some cases statistically significant differences were observed, suggesting a high variability of human annotation.

This report describes a reproduction of a human evaluation study evaluating automatically detected over- and undertranslations obtained using neural machine translation approaches. While the scope of the original study is much broader, a human evaluation is included as part of its system evaluation. We attempt an exact reproduction of this human evaluation, pertaining to translations on the the English-German language pair. While encountering minor logistical challenges, with all the source material being publicly available and some additional instructions provided by the original authors, we were able to reproduce the original experiment with only minor differences in the results.

We reproduced the human-based evaluation of the continuation of narratives task presented by Chakrabarty et al. (2022). This experiment is performed as part of the ReproNLP Shared Task on Reproducibility of Evaluations in NLP (Track C). Our main goal is to reproduce the original study under conditions as similar as possible. Specifically, we follow the original experimental design and perform human evaluations of the data from the original study, while describing the differences between the two studies. We then present the results of these two studies together with an analysis of similarities between them. Inter-annotator agreement (Krippendorff’s alpha) in the reproduction study is lower than in the original study, while the human evaluation results of both studies have the same trends, that is, our results support the findings in the original study.

We describe in this paper an attempt to reproduce some of the human of evaluation results from the paper “It’s not Rocket Science: Interpreting Figurative Language in Narratives”. In particular, we describe the methodology used to reproduce the chosen human evaluation, the challenges faced, and the results that were gathered. We will also make some recommendations on the learnings obtained from this reproduction attempt and what improvements are needed to enable more robust reproductions of future NLP human evaluations.

Terminology Alignment faces big challenges in NLP because of the dynamic nature of terms. Fortunately, over these last few years, Deep Learning models showed very good progress with several NLP tasks such as multilingual data resourcing, glossary building, terminology understanding. . . etc. In this work, we propose a new method for terminology alignment from a comparable corpus (Arabic/French languages) for the Algerian culture field. We aim to improve bilingual alignment based on contextual information of a term and to create a significant term bank i.e. a bilingual Arabic-French dictionary. We propose to create word embeddings for both Arabic and French languages using ELMO model focusing on contextual features of terms. Then, we mapp those embeddings using Seq2seq model. We use multilingual-BERT and All-MiniLM-L6 as baseline mod- els to compare terminology alignment results. Lastly we study the performance of these models by applying evaluation methods. Experimentation’s showed quite satisfying alignment results.

We propose a tool for the semi-automatic production of terminological databases, divided in the steps of corpus processing, terminology extraction, database population and management. With this tool it is possible to obtain a draft macrostructure (a lemma-list) and data for the microstructural level, such as grammatical (morphosyntactic patterns, gender, formation process) and semantic information (hypernyms, equivalence in another language, definitions and synonyms). In this paper we offer an overall description of the software and an evaluation of its performance, for which we used a linguistics corpus in English and Spanish.

Use of NLP Techniques in Translation by ChatGPT: Case Study Natural Language Processing (NLP) refers to a field of study within the domain of artificial intelligence (AI) and computational linguistics that focuses on the interaction between computers and human language. NLP seeks to develop computational models and algorithms capable of understanding, analyzing, and generating natural language text and speech (Brown et al., 1990). At its core, NLP aims to bridge the gap between human language and machine understanding by employing various techniques from linguistics, computer science, and statistics. It involves the application of linguistic and computational theories to process, interpret, and extract meaningful information from unstructured textual data (Bahdanau, Cho and Bengio, 2015). Researchers and practitioners in NLP employ diverse methodologies, including rule-based approaches, statistical models, machine learning techniques (such as neural networks), and more recently, deep learning architectures. These methodologies enable the development of robust algorithms that can learn from large-scale language data to improve the accuracy and effectiveness of language processing systems (Nilsson, 2010). NLP has numerous real-world applications across various domains, including information retrieval, virtual assistants, chatbots, social media analysis, sentiment monitoring, automated translation services, and healthcare, among others (kaynak). As the field continues to advance, NLP strives to overcome challenges such as understanding the nuances of human language, handling ambiguity, context sensitivity, and incorporating knowledge from diverse sources to enable machines to effectively communicate and interact with humans in a more natural and intuitive manner. Natural Language Processing (NLP) and translation are interconnected fields that share a symbiotic relationship, as NLP techniques and methodologies greatly contribute to the advancement and effectiveness of machine translation systems. NLP, a subfield of artificial intelligence (AI), focuses on the interaction between computers and human language. It encompasses a wide range of tasks, including text analysis, syntactic and semantic parsing, sentiment analysis, information extraction, and machine translation (Bahdanau, Cho and Bengio, 2014). NMT models employ deep learning architectures, such as recurrent neural networks (RNNs) and more specifically, long short-term memory (LSTM) networks, to learn the mapping between source and target language sentences. These models are trained on large-scale parallel corpora, consisting of aligned sentence pairs in different languages. The training process involves optimizing model parameters to minimize the discrepancy between predicted translations and human-generated translations (Wu et al., 2016) NLP techniques are crucial at various stages of machine translation. Preprocessing techniques, such as tokenization, sentence segmentation, and morphological analysis, help break down input text into meaningful linguistic units, making it easier for translation models to process and understand the content. Syntactic and semantic parsing techniques aid in capturing the structural and semantic relationships within sentences, improving the overall coherence and accuracy of translations. Furthermore, NLP-based methods are employed for handling specific translation challenges, such as handling idiomatic expressions, resolving lexical ambiguities, and addressing syntactic divergences between languages. For instance, statistical alignment models, based on NLP algorithms, enable the identification of correspondences between words or phrases in source and target languages, facilitating the generation of more accurate translations (kaynak). Several studies have demonstrated the effectiveness of NLP techniques in enhancing machine translation quality. For example, Bahdanau et al. (2015) introduced the attention mechanism, an NLP technique that enables NMT models to focus on relevant parts of the source sentence during translation. This attention mechanism significantly improved the translation quality of neural machine translation models. ChatGPT is a language model developed by OpenAI that utilizes the principles of Natural Language Processing (NLP) for various tasks, including translations. NLP is a field of artificial intelligence that focuses on the interaction between computers and human language. It encompasses a range of techniques and algorithms for processing, analyzing, and understanding natural language. When it comes to translation, NLP techniques can be applied to facilitate the conversion of text from one language to another. ChatGPT employs a sequence-to-sequence model, a type of neural network architecture commonly used in machine translation tasks. This model takes an input sequence in one language and generates a corresponding output sequence in the target language (OpenAI, 2023). The training process for ChatGPT involves exposing the model to large amounts of multilingual data, allowing it to learn patterns, syntax, and semantic relationships across different languages. This exposure enables the model to develop a general understanding of language structures and meanings, making it capable of performing translation tasks. To enhance translation quality, ChatGPT leverages the Transformer architecture, which has been highly successful in NLP tasks. Transformers utilize attention mechanisms, enabling the model to focus on different parts of the input sequence during the translation process. This attention mechanism allows the model to capture long-range dependencies and improve the overall coherence and accuracy of translations. Additionally, techniques such as subword tokenization, which divides words into smaller units, are commonly employed in NLP translation systems like ChatGPT. Subword tokenization helps handle out-of-vocabulary words and improves the model’s ability to handle rare or unknown words (GPT-4 Technical Report, 2023). As can be seen, there have been significant developments in artificial intelligence translations thanks to NLP. However, it is not possible to say that it has fully reached the quality of translation made by people. The only goal in artificial intelligence translations is to reach translations made by humans. In general, there are some fundamental differences between human and ChatGPT translations. Human-made translations and translations generated by ChatGPT (or similar language models) have several key differences (Kelly and Zetzsche, 2014; Koehn, 2010; Sutskever, Vinyals and Le, 2014; Costa-jussà and Fonollosa, 2018) Translation Quality: Human translators are capable of producing high-quality translations with a deep understanding of both the source and target languages. They can accurately capture the nuances, cultural references, idioms, and context of the original text. On the other hand, ChatGPT translations can sometimes be less accurate or may not fully grasp the intended meaning due to the limitations of the training data and the model’s inability to comprehend context in the same way a human can. While ChatGPT can provide reasonable translations, they may lack the finesse and precision of a human translator. Natural Language Processing: Human translators are skilled at processing and understanding natural language, taking into account the broader context, cultural implications, and the intended audience. They can adapt their translations to suit the target audience, tone, and purpose of the text. ChatGPT, although trained on a vast amount of text data, lacks the same level of natural language understanding. It often relies on pattern matching and statistical analysis to generate translations, which can result in less nuanced or contextually appropriate outputs. Subject Matter Expertise: Human translators often specialize in specific domains or subject areas, allowing them to have deep knowledge and understanding of technical or specialized terminology. They can accurately translate complex or industry-specific texts, ensuring the meaning is preserved. ChatGPT, while having access to a wide range of general knowledge, may struggle with domain-specific vocabulary or terminology, leading to inaccuracies or incorrect translations in specialized texts. Cultural Sensitivity: Human translators are well-versed in the cultural nuances of both the source and target languages. They can navigate potential pitfalls, adapt the translation to the cultural context, and avoid unintended offensive or inappropriate language choices. ChatGPT lacks this level of cultural sensitivity and may produce translations that are culturally tone-deaf or insensitive, as it lacks the ability to understand the subtleties and implications of language choices. Revision and Editing: Human translators go through an iterative process of revision and editing to refine their translations, ensuring accuracy, clarity, and quality. They can self-correct errors and refine their translations based on feedback or additional research. ChatGPT, while capable of generating translations, does not have the same ability to self-correct or improve based on feedback. It generates translations in a single pass, without the iterative refinement process that humans can employ. In summary, while ChatGPT can be a useful tool for generating translations, human-made translations generally outperform machine-generated translations in terms of quality, accuracy, contextuality, cultural sensitivity, and domain-specific expertise. In conclusion, NLP and machine translation are closely intertwined, with NLP providing essential tools, methodologies, and techniques that contribute to the development and improvement of machine translation systems. The integration of NLP methods has led to significant advancements in translation accuracy, fluency, and the ability to handle various linguistic complexities. As NLP continues to evolve, its impact on the field of machine translation is expected to grow, enabling the creation of more sophisticated and context-aware translation systems. On the basis of all this information, in this research, it is aimed to compare the translations from English to Turkish made by ChatGPT, one of the most advanced artificial intelligences, with the translations made by humans. In this context, an academic 1 page English text was chosen. The text was translated by both ChatGPT and a translator who is an academic in the field of translation and has 10 years of experience. Afterwards, two different translations were examined comparatively by 5 different translators who are experts in their fields. Semi-structured in-depth interviews were conducted with these translators. The aim of this study is to reveal the role of artificial intelligence tools in translation, which are increasing day by day and suggesting that there will be no need for language learning in the future. On the other hand, many translators argue that artificial intelligence and human translations can be understood. Therefore, if artificial intelligence is successful, there will be no profession called translator in the future. This research seems to be very useful in terms of shedding light on the future. The method of this research is semi-structured in-depth interview. References Bahdanau, D., Cho, K. and Bengio Y. (2015). Neural machine translation by jointly learning to align and translate. In International Conference on Learning Representations. Brown, P. F., Cocke, J., Pietra, S. A. D., Pietra, V. J. D., Jelinek, F., Lafferty, J. D., Mercer, R. L., and Roossin, P. S. A. (1990) statistical approach to machine translation. Computational linguistics 16, 2, 79–85. Costa-jussà, M. R., & Fonollosa, J. A. R. (2018). “An Overview of Neural Machine Translation.” IEEE Transactions on Neural Networks and Learning Systems. GPT-4 Technical Report (2023). https://arxiv.org/abs/2303.08774. Kelly, N. and Zetzsche, J. (2014). Found in Translation: How Language Shapes Our Lives and Transforms the World. USA: Penguin Book. Koehn, P. (2010). “Statistical Machine Translation.” Cambridge University Press. Nilsson, N. J. (2010). The Quest For AI- A History Of Ideas And Achievements. http://ai.standford.edu/ nilsson/. OpenAI (2023). https://openai.com/blog/chatgpt/. Sutskever, I., Vinyals, O., & Le, Q. V. (2014). “Sequence to Sequence Learning with Neural Networks.” Advances in Neural Information Processing Systems. Wu,Y. Schuster, M., Chen, Z., Le, Q. V. and Norouzi M. (2016). Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. https://arxiv.org/pdf/1609.08144.pdf.

In the translation process, terminological resources are used to solve translation problems, so information on terminological equivalence is crucial to make the most appropriate choices in terms of translation equivalence. In the context of Machine translation, indeed, neural models have improved the state-of-the-art in Machine Translation considerably in recent years. However, they still underperform in domain-specific fields and in under-resourced languages. This is particularly evident in translating legal terminology for Arabic, where current Machine Translation outputs do not adhere to the contextual, linguistic, cultural, and terminological constraints posed by translating legal terms in Arabic. In this paper, we conduct a comparative qualitative evaluation and comprehensive error analysis on legal terminology translation in Phrase-Based Statistical Machine Translation and Neural Machine Translation in two translation language pairs: Arabic-English and Arabic-French. We propose an error typology taking the legal terminology translation from Arabic into account. We demonstrate our findings, highlighting the strengths and weaknesses of both approaches in the area of legal terminology translation for Arabic. We also introduce a multilingual gold standard dataset that we developed using our Arabic legal corpus. This dataset serves as a reliable benchmark and/or reference during the evaluation process to decide the degree of adequacy and fluency of the Phrase-Based Statistical Machine Translation and Neural Machine Translation systems.

Implementing technology in a modern-day classroom is an ongoing challenge. In this paper, we created a system for an automatic assessment of student answers using Latent Semantic Analysis (LSA) – a method with an underlying assumption that words with similar meanings will appear in the same contexts. The system will be used within digital lexical flash-cards for L2 vocabulary acquisition in a CLIL classroom. Results presented in this paper indicate that while LSA does well in creating semantic spaces for longer texts, it somewhat struggles with detecting topics in short texts. After obtaining LSA semantic spaces, answer accuracy was assessed by calculating the cosine similarity between a student’s answer and the golden standard. The answers were classified by accuracy using KNN, for both binary and multinomial classification. The results of KNN classification are as follows: precision P = 0.73, recall R = 1.00, F1 = 0.85 for binary classification, and P = 0.50, R = 0.47, F1 = 0.46 score for the multinomial classifier. The results are to be taken with a grain of salt, due to a small test and training dataset.

This paper represents a description of Bulgarian verbal computer terms with a view to the specifics of their translation in English. The study employs a subset of 100 verbs extracted from the Bulgarian WordNet (BulNet) and from the internet. The analysis of their syntactic and semantic structure is a part of a study of the general lexis of Bulgarian. The aim of the paper is to (1) identify some problem areas of the description and translation of general lexis verbs, (2) offer an approach to the semantic description of metaphor-based terms from the perspective of Frame Semantics; (3) raise questions about the definition of general lexis with respect to Bulgarian and across languages.

Initial work has been done to address fake news detection and misrepresentation of news in the Bengali language. However, no work in Bengali yet addresses the identification of specific claims in social media news that falsely manipulate a related news article. At this point, this problem has been tackled in English and a few other languages, but not in the Bengali language. In this paper, we curate a dataset of social media content labeled with information manipulation relative to reference articles, called BanMANI. The dataset collection method we describe works around the limitations of the available NLP tools in Bangla. We expect these techniques will carry over to building similar datasets in other low-resource languages. BanMANI forms the basis both for evaluating the capabilities of existing NLP systems and for training or fine-tuning new models specifically on this task. In our analysis, we find that this task challenges current LLMs both under zero-shot and fine-tuned set- things

This study, submitted to the BUCC2023 shared task on bilingual term alignment in comparable specialised corpora, introduces a supervised, feature-based classification approach. The approach employs both static cross-lingual embeddings and contextual multilingual embeddings, combined with surface-level indicators such as Levenshtein distance and term length, as well as linguistic information. Results exhibit improved performance over previous methodologies, illustrating the merit of integrating diverse features. However, the error analysis also reveals remaining challenges.

This paper explores a post-suppositional view on wh-questions and their answers with dynamic semantics. Inspired by Brasoveanu (2013); Charlow (2017); Bumford (2017), I propose a unified treatment of items like modified numerals, focus items, and wh-items: they (i) introduce a discourse referent (dref) in a non-deterministic way and (ii) impose definiteness tests (and additional tests) in a delayed, post-suppositional manner at the sentential / discourse level. Thus, with a question like “who smiled”, the (maximally informative) dref “the one(s) who smiled” is derived. A short answer like “Mary and Max” is considered another post-supposition-like, delayed test, checking whether the dref “the one(s) who smiled” is identical to (or includes) the sum “Mary⊕Max”. I analyze various question-related phenomena to see how far this proposal can go.

The paper extends a referentially transparent approach which has been successfully applied to the analysis of declarative quantified NPs to wh-phrases. This uses data from dialogical phenomena such as clarification interaction, anaphora, and incrementality as a guide to the design of wh-phrase meanings.

The sort of denotation a sentence is assigned is typically motivated by assumptions about the discourse function of sentences of that kind. For example, the notion that utterances which are functionally inquisitive (asking a question) suggest denotations which are semantically inquisitive (expressing the multiple licit responses to that question) is the cornerstone of interrogative meaning in frameworks like Alternative Semantics (Hamblin, 1973) and Inquisitive Semantics (Ciardelli et al., 2018). This paper argues that at least some kinds of questions systematically do not involve utterances with inquisitive content, based on novel observations of the Estonian discourse particle ega. Though ega is often labeled a ‘question particle’, it is used in both assertions and questions with sharply divergent discourse effects. I suggest that the relevant difference between assertive and questioning uses of ega is not semantic or sentence type-related, but rather reflects an interaction between a unified semantics for declaratives ega-sentences and different contexts of use. I then show that if we assume that ega presupposes that some aspect of the discourse context implicates the negation of ega’s prejacent, and that it occurs only in declarative sentences, we can derive its interpretation across a range of contexts: with the right combination of ingredients, we can ask questions with semantically uninquisitive sentences.

This paper investigates Farsi particle ‘mage’ in interrogatives, including both polar and constituent/Wh questions. I will show that ‘mage’ requires both contextual evidence and speaker’s prior belief in the sense that they contradict each other. While in polar questions (PQs) both types of bias can be straightforwardly expressed through the uttered proposition (cf. Mameni 2010), Wh-questions (WhQs) do not provide such a propositional object. To capture this difference, I propose Answerhood as the relevant notation that provides the necessary object source for ‘mage’ (inspired by Theiler 2021). The proposal establishes the felicity conditions and the meaning of ‘mage’ in relation to the (contextually) restricted answerhood in both polar and constituent questions.

This paper investigates the clausal embedding pattern of the Mandarin verb “xiang” (think) and reveals its internal anti-interrogative nature, with the possibility of “xiang Q” in certain cases. Through various stativity tests, I establish that the results are consistent with the generalization proposed by Özyıldız(2021), with “minor” deviations observed in the stativity of “xiang P” and the correlation with neg-raising. Additionally, I employ a semantic shift perspective to explain instances of neg-raising failure. Overall, this study sheds light on the unique characteristics of the verb “xiang” and contributes to a better cross-linguistic understanding of CP selection.

Prior studies on spoken languages have shown that indefinite and interrogative pronouns may be formally very similar. Our research aims to understand if sign languages exhibit this type of affinity. This paper presents an overview of the phenomenon and reports on the results of two studies: a cross-linguistic survey based on a sample of 30 sign languages and an empirical investigation conducted with three deaf consultants of Catalan Sign Language (LSC). Our research shows that, in sign languages, certain signs have both existential and interrogative readings and it identifies the environments that make existential interpretations available in LSC.

This paper summarizes the second edition of the shared task on multilingual coreference resolution, held with the CRAC 2023 workshop. Just like last year, participants of the shared task were to create trainable systems that detect mentions and group them based on identity coreference; however, this year’s edition uses a slightly different primary evaluation score, and is also broader in terms of covered languages: version 1.1 of the multilingual collection of harmonized coreference resources CorefUD was used as the source of training and evaluation data this time, with 17 datasets for 12 languages. 7 systems competed in this shared task.

The paper presents two multilingual coreference resolution systems submitted for the CRAC Shared Task 2023. The DFKI-Adapt system achieves 61.86 F1 score on the shared task test data, outperforming the official baseline by 4.9 F1 points. This system uses a combination of different features and training settings, including character embeddings, adapter modules, joint pre-training and loss-based re-training. We provide evaluation for each of the settings on 12 different datasets and compare the results. The other submission DFKI-MPrompt uses a novel approach that involves prompting for mention generation. Although the scores achieved by this model are lower compared to the baseline, the method shows a new way of approaching the coreference task and provides good results with just five epochs of training.

In morphologically rich languages, words consist of morphemes containing deeper information in morphology, and thus such languages may necessitate the use of morpheme-level representations as well as word representations. This study introduces a neural multilingual end-to-end coreference resolution system by incorporating morphological information in transformer-based word embeddings on the baseline model. This proposed model participated in the Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023). Including morphological information explicitly into the coreference resolution improves the performance, especially in morphologically rich languages (e.g., Catalan, Hungarian, and Turkish). The introduced model outperforms the baseline system by 2.57 percentage points on average by obtaining 59.53% CoNLL F-score.

We present CorPipe, the winning entry to the CRAC 2023 Shared Task on Multilingual Coreference Resolution. Our system is an improved version of our earlier multilingual coreference pipeline, and it surpasses other participants by a large margin of 4.5 percent points. CorPipe first performs mention detection, followed by coreference linking via an antecedent-maximization approach on the retrieved spans. Both tasks are trained jointly on all available corpora using a shared pretrained language model. Our main improvements comprise inputs larger than 512 subwords and changing the mention decoding to support ensembling. The source code is available at https://github.com/ufal/crac2023-corpipe.

Our submission to the CRAC 2023 shared task, described herein, is an adapted entity-ranking model jointly trained on all 17 datasets spanning 12 languages. Our model outperforms the shared task baselines by a difference in F1 score of +8.47, achieving an ultimate F1 score of 65.43 and fourth place in the shared task. We explore design decisions related to data preprocessing, the pretrained encoder, and data mixing.

Natural language processing (NLP) has recently gained relevance within financial institutions by providing highly valuable insights into companies and markets’ financial documents. However, the landscape of the financial domain presents extra challenges for NLP, due to the complexity of the texts and the use of specific terminology. Generalist language models tend to fall short in tasks specifically tailored for finance, even when using large language models (LLMs) with great natural language understanding and generative capabilities. This paper presents a study on LLM adaptation methods targeted at the financial domain and with high emphasis on financial sentiment analysis. To this purpose, two foundation models with less than 1.5B parameters have been adapted using a wide range of strategies. We show that through careful fine-tuning on both financial documents and instructions, these foundation models can be adapted to the target domain. Moreover, we observe that small LLMs have comparable performance to larger scale models, while being more efficient in terms of parameters and data. In addition to the models, we show how to generate artificial instructions through LLMs to augment the number of samples of the instruction dataset.

In this paper, we present ECL, a novel multimodal dataset containing the textual and numerical data from corporate 10K filings and associated binary bankruptcy labels. Furthermore, we develop and critically evaluate several classical and neural bankruptcy prediction models using this dataset. Our findings suggest that the information contained in each data modality is complementary for bankruptcy prediction. We also see that the binary bankruptcy prediction target does not enable our models to distinguish next year bankruptcy from an unhealthy financial situation resulting in bankruptcy in later years. Finally, we explore the use of LLMs in the context of our task. We show how GPT-based models can be used to extract meaningful summaries from the textual data but zero-shot bankruptcy prediction results are poor. All resources required to access and update the dataset or replicate our experiments are available on github.com/henriarnoUG/ECL.

The purpose of this paper is to construct a model for the generation of sophisticated headlines pertaining to stock price fluctuation articles, derived from the articles’ content. With respect to this headline generation objective, this paper solves three distinct tasks: in addition to the task of generating article headlines, two other tasks of extracting security names, and ascertaining the trajectory of stock prices, whether they are rising or declining. Regarding the headline generation task, we also revise the task as the model utilizes the outcomes of the security name extraction and rise/decline determination tasks, thereby for the purpose of preventing the inclusion of erroneous security names. We employed state-of-the-art pre-trained models from the field of natural language processing, fine-tuning these models for each task to enhance their precision. The dataset utilized for fine-tuning comprises a collection of articles delineating the rise and decline of stock prices. Consequently, we achieved remarkably high accuracy in the dual tasks of security name extraction and stock price rise or decline determination. For the headline generation task, a significant portion of the test data yielded fitting headlines.

Audit reports are a window to the financial health of a company and hence gauging coverage of various audit aspects in them is important. In this paper, we aim at determining an audit report’s coverage through classification of its sentences into multiple domain specific classes. In a weakly supervised setting, we employ a rule-based approach to automatically create training data for a BERT-based multi-label classifier. We then devise an ensemble to combine both the rule based and classifier approaches. Further, we employ two novel ways to improve the ensemble’s generalization: (i) through an active learning based approach and, (ii) through a LLM based review. We demonstrate that our proposed approaches outperform several baselines. We show utility of the proposed approaches to measure audit coverage on a large dataset of 2.8K audit reports.

Relation extraction (RE) is a crucial task in natural language processing (NLP) that aims to identify and classify relationships between entities mentioned in text. In the financial domain, relation extraction plays a vital role in extracting valuable information from financial documents, such as news articles, earnings reports, and company filings. This paper describes our solution to relation extraction on one such dataset REFinD. The dataset was released along with shared task as a part of the Fourth Workshop on Knowledge Discovery from Unstructured Data in Financial Services, co-located with SIGIR 2023. In this paper, we employed OpenAI models under the framework of in-context learning (ICL). We utilized two retrieval strategies to find top K relevant in-context learning demonstrations / examples from training data for a given test example. The first retrieval mechanism, we employed, is a learning-free dense retriever and the other system is a learning-based retriever. We were able to achieve 3rd rank overall. Our best F1-score is 0.718.

Assessing a company’s sustainable development goes beyond just financial metrics; the inclusion of environmental, social, and governance (ESG) factors is becoming increasingly vital. The ML-ESG shared task series seeks to pioneer discussions on news-driven ESG ratings, drawing inspiration from the MSCI ESG rating guidelines. In its second edition, ML-ESG-2 emphasizes impact type identification, offering datasets in four languages: Chinese, English, French, and Japanese. Of the 28 teams registered, 8 participated in the official evaluation. This paper presents a comprehensive overview of ML-ESG-2, detailing the dataset specifics and summarizing the performance outcomes of the participating teams.

The paper presents a concise summary of our work for the ML-ESG-2 shared task, exclusively on the Chinese and English datasets. ML-ESG-2 aims to ascertain the influence of news articles on corporations, specifically from an ESG perspective. To this end, we generally explored the capability of key information for impact identification and experimented with various techniques at different levels. For instance, we attempted to incorporate important information at the word level with TF-IDF, at the sentence level with TextRank, and at the document level with summarization. The final results reveal that the one with GPT-4 for summarisation yields the best predictions.

With the growing interest in Green Investing, Environmental, Social, and Governance (ESG) factors related to Institutions and financial entities has become extremely important for investors. While the classification of potential ESG factors is an important issue, identifying whether the factors positively or negatively impact the Institution is also a key aspect to consider while making evaluations for ESG scores. This paper presents our solution to identify ESG impact types in four languages (English, Chinese, Japanese, French) released as shared tasks during the FinNLP workshop at the IJCNLP-AACL-2023 conference. We use a combination of translation, masked language modeling, paraphrasing, and classification to solve this problem and use a generalized pipeline that performs well across all four languages. Our team ranked 1st in the Chinese and Japanese sub-tasks.

In this paper, we present our solutions to the ML-ESG-2 shared task which is co-located with the FinNLP workshop at IJCNLP-AACL-2023. The task proposes an objective of binary classification of ESG-related news based on what type of impact they can have on a company - Risk or Opportunity. We report the results of three systems, which ranked 2nd, 9th, and 10th in the final leaderboard for the English language, with the best solution achieving over 0.97 in F1 score.

This paper presents our findings in the ML-ESG-2 task, which focused on classifying a news snippet of various languages as “Risk” or “Opportunity” in the ESG (Environmental, Social, and Governance) context. We experimented with data augmentation and translation facilitated by Large Language Models (LLM). We found that augmenting the English dataset did not help to improve the performance. By fine-tuning RoBERTa models with the original data, we achieved the top position for the English and second place for the French task. In contrast, we could achieve comparable results on the French dataset by solely using the English translation, securing the third position for the French task with only marginal F1 differences to the second-place model.

In this paper, we describe our approach to the ML-ESG-2 shared task, co-located with the FinNLP workshop at IJCNLP-AACL-2023. The task aims at classifying news articles into categories reflecting either “Opportunity” or “Risk” from an ESG standpoint for companies. Our innovative methodology leverages two distinct systems for optimal text classification. In the initial phase, we engage in prompt engineering, working in conjunction with semantic similarity and using the Claude 2 LLM. Subsequently, we apply fine-tuning techniques to the Llama 2 and Dolly LLMs to enhance their performance. We report the results of five different approaches in this paper, with our top models ranking first in the French category and sixth in the English category.

In this paper, we discuss our (Team HHU’s) submission to the Multi-Lingual ESG Impact Type Identification task (ML-ESG-2). The goal of this task is to determine if an ESG-related news article represents an opportunity or a risk. We use an adapter-based framework in order to train multiple adapter modules which capture different parts of the knowledge present in the training data. Experimenting with various Adapter Fusion setups, we focus both on combining the ESG-aspect-specific knowledge, and on combining the language-specific-knowledge. Our results show that in both cases, it is possible to effectively compose the knowledge in order to improve the impact type determination.

In the evolving landscape of Environmental, Social, and Corporate Governance (ESG) impact assessment, the ML-ESG-2 shared task proposes identifying ESG impact types. To address this challenge, we present a comprehensive system leveraging ensemble learning techniques, capitalizing on early and late fusion approaches. Our approach employs four distinct models: mBERT, FlauBERT-base, ALBERT-base-v2, and a Multi-Layer Perceptron (MLP) incorporating Latent Semantic Analysis (LSA) and Term Frequency-Inverse Document Frequency (TF-IDF) features. Through extensive experimentation, we find that our early fusion ensemble approach, featuring the integration of LSA, TF-IDF, mBERT, FlauBERT-base, and ALBERT-base-v2, delivers the best performance. Our system offers a comprehensive ESG impact type identification solution, contributing to the responsible and sustainable decision-making processes vital in today’s financial and corporate governance landscape.

Although image captioning has a vast array of applications, it has not reached its full potential in languages other than English. Arabic, for instance, although the native language of more than 400 million people, remains largely underrepresented in this area. This is due to the lack of labeled data and powerful Arabic generative models. We alleviate this issue by presenting a novel vision-language model dedicated to Arabic, dubbed Violet. Our model is based on a vision encoder and a Gemini text decoder that maintains generation fluency while allowing fusion between the vision and language components. To train our model, we introduce a new method for automatically acquiring data from available English datasets. We also manually prepare a new dataset for evaluation. Violet performs sizeably better than our baselines on all of our evaluation datasets. For example, it reaches a CIDEr score of 61.2 on our manually annotated dataset and achieves an improvement of 13 points on Flickr8k.

This paper presents Nâbra (نَبْرَة), a corpora of Syrian Arabic dialects with morphological annotations. A team of Syrian natives collected more than 6K sentences containing about 60K words from several sources including social media posts, scripts of movies and series, lyrics of songs and local proverbs to build Nâbra. Nâbra covers several local Syrian dialects including those of Aleppo, Damascus, Deir-ezzur, Hama, Homs, Huran, Latakia, Mardin, Raqqah, and Suwayda. A team of nine annotators annotated the 60K tokens with full morphological annotations across sentence contexts. We trained the annotators to follow methodological annotation guidelines to ensure unique morpheme annotations, and normalized the annotations. F1 and 𝜅 agreement scores ranged between 74% and 98% across features, showing the excellent quality of Nâbra annotations. Our corpora are open-source and publicly available as part of the Currasat portal https://sina.birzeit.edu/currasat.

Arabic is one of the most globally spoken languages with more than 313 million speakers worldwide. Arabic handwriting is known for its cursive nature and the variety of writing styles used. Despite the increase in effort to digitize artistic and historical elements, no public dataset was released to deal with Arabic text recognition for realistic manuscripts and calligraphic text. We present the Handwriting Identification of Manuscripts and Calligraphy in Arabic (HICMA) dataset as the first publicly available dataset with real-world and diverse samples of Arabic handwritten text in manuscripts and calligraphy. With more than 5,000 images across five different styles, the HICMA dataset includes image-text pairs and style labels for all images. We further present a comparison of the current state-of-the-art optical character recognition models in Arabic and benchmark their performance on the HICMA dataset, which serves as a baseline for future works. Both the HICMA dataset and its benchmarking tool are made available to the public under the CC BY-NC 4.0 license in the hope that the presented work opens the door to further enhancements of complex Arabic text recognition.

As Electronic Health Records (EHR) become ubiquitous in healthcare systems worldwide, including in Arabic-speaking countries, the dual imperative of safeguarding patient privacy and leveraging data for research and quality improvement grows. This paper presents a first-of-its-kind automated de-identification pipeline for medical text specifically tailored for the Arabic language. This includes accurate medical Named Entity Recognition (NER) for identifying personal information; data obfuscation models to replace sensitive entities with fake entities; and an implementation that natively scales to large datasets on commodity clusters. This research makes two contributions. First, we adapt two existing NER architectures— BERT For Token Classification (BFTC) and BiLSTM-CNN-Char – to accommodate the unique syntactic and morphological characteristics of the Arabic language. Comparative analysis suggests that BFTC models outperform Bi-LSTM models, achieving higher F1 scores for both identifying and redacting personally identifiable information (PII) from Arabic medical texts. Second, we augment the deep learning models with a contextual parser engine to handle commonly missed entities. Experiments show that the combined pipeline demonstrates superior performance with micro F1 scores ranging from 0.94 to 0.98 on the test dataset, which is a translated version of the i2b2 2014 de-identification challenge, across 17 sensitive entities. This level of accuracy is in line with that achieved with manual de-identification by domain experts, suggesting that a fully automated and scalable process is now viable.

We present ArTST, a pre-trained Arabic text and speech transformer for supporting open-source speech technologies for the Arabic language. The model architecture follows the unified-modal framework, SpeechT5, that was recently released for English, and is focused on Modern Standard Arabic (MSA), with plans to extend the model for dialectal and code-switched Arabic in future editions. We pre-trained the model from scratch on MSA speech and text data, and fine-tuned it for the following tasks: Automatic Speech Recognition (ASR), Text-To-Speech synthesis (TTS), and spoken dialect identification. In our experiments comparing ArTST with SpeechT5, as well as with previously reported results in these tasks, ArTST performs on a par with or exceeding the current state-of-the-art in all three tasks. Moreover, we find that our pre-training is conducive for generalization, which is particularly evident in the low-resource TTS task. The pre-trained model as well as the fine-tuned ASR and TTS models are released for research use.

Despite the purported multilingual proficiency of instruction-finetuned large language models (LLMs) such as ChatGPT and Bard, the linguistic inclusivity of these models remains insufficiently explored. Considering this constraint, we present a thorough assessment of Bard and ChatGPT (encompassing both GPT-3.5 and GPT-4) regarding their machine translation proficiencies across ten varieties of Arabic. Our evaluation covers diverse Arabic varieties such as Classical Arabic (CA), Modern Standard Arabic (MSA), and several country-level dialectal variants. Our analysis indicates that LLMs may encounter challenges with dialects for which minimal public datasets exist, but on average are better translators of dialects than existing commercial systems. On CA and MSA, instruction-tuned LLMs, however, trail behind commercial systems such as Google Translate. Finally, we undertake a human-centric study to scrutinize the efficacy of the relatively recent model, Bard, in following human instructions during translation tasks. Our analysis reveals a circumscribed capability of Bard in aligning with human instructions in translation contexts. Collectively, our findings underscore that prevailing LLMs remain far from inclusive, with only limited ability to cater for the linguistic and cultural intricacies of diverse communities.

In this work, we approach the problem of Qur’anic information retrieval (IR) in Arabic and English. Using the latest state-of-the-art methods in neural IR, we research what helps to tackle this task more efficiently. Training retrieval models requires a lot of data, which is difficult to obtain for training in-domain. Therefore, we commence with training on a large amount of general domain data and then continue training on in-domain data. To handle the lack of in-domain data, we employed a data augmentation technique, which considerably improved results in MRR@10 and NDCG@5 metrics, setting the state-of-the-art in Qur’anic IR for both English and Arabic. The absence of an Islamic corpus and domain-specific model for IR task in English motivated us to address this lack of resources and take preliminary steps of the Islamic corpus compilation and domain-specific language model (LM) pre-training, which helped to improve the performance of the retrieval models that use the domain-specific LM as the shared backbone. We examined several language models (LMs) in Arabic to select one that efficiently deals with the Qur’anic IR task. Besides transferring successful experiments from English to Arabic, we conducted additional experiments with retrieval task in Arabic to amortize the scarcity of general domain datasets used to train the retrieval models. Handling Qur’anic IR task combining English and Arabic allowed us to enhance the comparison and share valuable insights across models and languages.

Text summarization has been intensively studied in many languages, and some languages have reached advanced stages. Yet, Arabic Text Summarization (ATS) is still in its developing stages. Existing ATS datasets are either small or lack diversity. We build, LANS, a large-scale and diverse dataset for Arabic Text Summarization task. LANS offers 8.4 million articles and their summaries extracted from newspapers websites’ metadata between 1999 and 2019. The high-quality and diverse summaries are written by journalists from 22 major Arab newspapers and include an eclectic mix of at least more than 7 topics from each source. We conduct an intrinsic evaluation on LANS by both automatic and human evaluations. Human evaluation of 1,000 random samples reports 95.4% accuracy for our collected summaries, and automatic evaluation quantifies the diversity and abstractness of the summaries.

Large language models (LLMs) finetuned to follow human instruction have recently exhibited significant capabilities in various English NLP tasks. However, their performance in grammatical error correction (GEC), especially on languages other than English, remains significantly unexplored. In this work, we evaluate the abilities of instruction finetuned LLMs in Arabic GEC, a complex task due to Arabic’s rich morphology. Our findings suggest that various prompting methods, coupled with (in-context) few-shot learning, demonstrate considerable effectiveness, with GPT-4 achieving up to 65.49 F1 score under expert prompting (approximately 5 points higher than our established baseline). Despite these positive results, we find that instruction finetuned models, regardless of their size, are still outperformed by fully finetuned ones, even if they are significantly smaller in size. This disparity highlights substantial room for improvements for LLMs. Inspired by methods used in low-resource machine translation, we also develop a method exploiting synthetic data that significantly outperforms previous models on two standard Arabic benchmarks. Our best model achieves a new SOTA on Arabic GEC, with 73.29 and 73.26 F1 on the 2014 and 2015 QALB datasets, respectively, compared to peer-reviewed published baselines.

Recent advancements in self-supervised speech-representation learning for automatic speech recognition (ASR) approaches have significantly improved the results on many benchmarks with low-cost data labeling. In this paper, we train two self-supervised frameworks for ASR, namely wav2vec, and data2vec, in which we conduct multiple experiments and analyze their results. Furthermore, we introduce Aswat dataset, which covers multiple genres and features speakers with vocal variety. Aswat contains 732 hours of clean Arabic speech that can be used in the pretraining task for learning latent speech representations, which results in achieving a lower word error rate (WER) in Arabic ASR. We report the baseline results and achieve state-of-the-art WERs of 11.7% and 10.3% on Common Voice (CV) and the second round of Multi-Genre Broadcast (MGB-2) respectively, as a result of including our dataset Aswat.

While significant progress has been made in benchmarking Large Language Models (LLMs) across various tasks, there is a lack of comprehensive evaluation of their abilities in responding to multi-turn instructions in less-commonly tested languages like Arabic. Our paper offers a detailed examination of the proficiency of open LLMs in such scenarios in Arabic. Utilizing a customized Arabic translation of the MT-Bench benchmark suite, we employ GPT-4 as a uniform evaluator for both English and Arabic queries to assess and compare the performance of the LLMs on various open-ended tasks. Our findings reveal variations in model responses on different task categories, e.g., logic vs. literacy, when instructed in English or Arabic. We find that fine-tuned base models using multilingual and multi-turn datasets could be competitive to models trained from scratch on multilingual data. Finally, we hypothesize that an ensemble of small, open LLMs could perform competitively to proprietary LLMs on the benchmark.

In this paper, we study the transferability of Named Entity Recognition (NER) models between Arabic dialects. This question is important because the available manually-annotated resources are not distributed equally across dialects: Modern Standard Arabic (MSA) is much richer than other dialects for which little to no datasets exist. How well does a NER model, trained on MSA, perform on other dialects? To answer this question, we construct four datasets. The first is an MSA dataset extracted from the ACE 2005 corpus. The others are datasets for Egyptian, Morocan and Syrian which we manually annotate following the ACE guidelines. We train a span-based NER model on top of a pretrained language model (PLM) encoder on the MSA data and study its performance on the other datasets in zero-shot settings. We study the performance of multiple PLM encoders from the literature and show that they achieve acceptable performance with no annotation effort. Our annotations and models are publicly available (https://github.com/niamaelkhbir/Arabic-Cross-Dialectal-NER).

Product information in e-commerce is usually localized using machine translation (MT) systems. Arabic language has rich morphology and dialectal variations, so Arabic MT in e-commerce training requires a larger volume of data from diverse data sources; Given the dynamic nature of e-commerce, such data needs to be acquired periodically to update the MT. Consequently, validating the quality of training data periodically within an industrial setting presents a notable challenge. Meanwhile, the performance of MT systems is significantly impacted by the quality and appropriateness of the training data. Hence, this study first examines the Arabic MT in e-commerce and investigates the data quality challenges for English-Arabic MT in e-commerce then proposes heuristics-based and topic-based data selection approaches to improve MT for product information. Both online and offline experiment results have shown our proposed approaches are effective, leading to improved shopping experiences for customers.

Extracting and disambiguating geolocation information from social media data enables effective disaster management, as it helps response authorities; for example, locating incidents for planning rescue activities and affected people for evacuation. Nevertheless, the dearth of resources and tools hinders the development and evaluation of Location Mention Disambiguation (LMD) models in the disaster management domain. Consequently, the LMD task is greatly understudied, especially for the low resource languages such as Arabic. To fill this gap, we introduce IDRISI-D, the largest to date English and the first Arabic public LMD datasets. Additionally, we introduce a modified hierarchical evaluation framework that offers a lenient and nuanced evaluation of LMD systems. We further benchmark IDRISI-D datasets using representative baselines and show the competitiveness of BERT-based models.

We present CamelParser2.0, an open-source Python-based Arabic dependency parser targeting two popular Arabic dependency formalisms, the Columbia Arabic Treebank (CATiB), and Universal Dependencies (UD). The CamelParser2.0 pipeline handles the processing of raw text and produces tokenization, part-of-speech and rich morphological features. As part of developing CamelParser2.0, we explore many system design hyper-parameters, such as parsing model architecture and pretrained language model selection, achieving new state-of-the-art performance across diverse Arabic genres under gold and predicted tokenization settings.

Bilingual Lexical Induction (BLI) is a core challenge in NLP, it relies on the relative isomorphism of individual embedding spaces. Existing attempts aimed at controlling the relative isomorphism of different embedding spaces fail to incorporate the impact of semantically related words in the model training objective. To address this, we propose GARI that combines the distributional training objectives with multiple isomorphism losses guided by the graph attention network. GARI considers the impact of semantical variations of words in order to define the relative isomorphism of the embedding spaces. Experimental evaluation using the Arabic language data set shows that GARI outperforms the existing research by improving the average P@1 by a relative score of up to 40.95% and 76.80% for in-domain and domain mismatch settings respectively.

We present ArTrivia, a new Arabic question-answering dataset consisting of more than 10,000 question-answer pairs along with relevant passages, covering a wide range of 18 diverse topics in Arabic. We created our dataset using a newly proposed pipeline that leverages diverse structured data sources from Arabic Wikipedia. Moreover, we conducted a comprehensive statistical analysis of ArTrivia and assessed the performance of each component in our pipeline. Additionally, we compared the performance of ArTrivia against the existing TyDi QA dataset using various experimental setups. Our analysis highlights the significance of often overlooked aspects in dataset creation, such as answer normalization, in enhancing the quality of QA datasets. Our evaluation also shows that ArTrivia presents more challenging and out-of-distribution questions to TyDi, raising questions about the feasibility of using ArTrivia as a complementary dataset to TyDi.

In digital communication, emoji are essential in decoding nuances such as irony, sarcasm, and humour. However, their incorporation in Arabic natural language processing (NLP) has been cautious because of the perceived complexities of the Arabic language. This paper introduces ArSarcasMoji, a dataset of 24,630 emoji-augmented texts, with 17. 5% that shows irony. Through our analysis, we highlight specific emoji patterns paired with sentiment roles that denote irony in Arabic texts. The research counters prevailing notions, emphasising the importance of emoji’s role in understanding Arabic textual irony, and addresses their potential for accurate irony detection in Arabic digital content.

Wikipedia articles are a widely used source of training data for Natural Language Processing (NLP) research, particularly as corpora for low-resource languages like Arabic. However, it is essential to understand the extent to which these corpora reflect the representative contributions of native speakers, especially when many entries in a given language are directly translated from other languages or automatically generated through automated mechanisms. In this paper, we study the performance implications of using inorganic corpora that are not representative of native speakers and are generated through automated techniques such as bot generation or automated template-based translation. The case of the Arabic Wikipedia editions gives a unique case study of this since the Moroccan Arabic Wikipedia edition (ARY) is small but representative, the Egyptian Arabic Wikipedia edition (ARZ) is large but unrepresentative, and the Modern Standard Arabic Wikipedia edition (AR) is both large and more representative. We intrinsically evaluate the performance of two main NLP upstream tasks, namely word representation and language modeling, using word analogy evaluations and fill-mask evaluations using our two newly created datasets: Arab States Analogy Dataset (ASAD) and Masked Arab States Dataset (MASD). We demonstrate that for good NLP performance, we need both large and organic corpora; neither alone is sufficient. We show that producing large corpora through automated means can be a counter-productive, producing models that both perform worse and lack cultural richness and meaningful representation of the Arabic language and its native speakers.

Understanding Arabic text and generating human-like responses is a challenging task. While many researchers have proposed models and solutions for individual problems, there is an acute shortage of a comprehensive Arabic natural language generation toolkit that is capable of handling a wide range of tasks. In this work, we present a robust Arabic text-to-text Transformer model, namely AraT5v2, methodically trained on extensive and diverse data, utilizing an extended sequence length of 2,048 tokens. We explore various pretraining strategies including unsupervised, supervised, and joint pertaining, under both single and multitask settings. Our models outperform competitive baselines with large margins. We take our work one step further by developing and publicly releasing OCTOPUS, a Python-based package and command-line toolkit tailored for eight Arabic generation tasks all exploiting a single model. We provide a link to the models and the toolkit through our public repository.

Recent advances in the space of Arabic large language models have opened up a wealth of potential practical applications. From optimal training strategies, large scale data acquisition and continuously increasing NLP resources, the Arabic LLM landscape has improved in a very short span of time, despite being plagued by training data scarcity and limited evaluation resources compared to English. In line with contributing towards this ever-growing field, we introduce AlGhafa, a new multiple-choice evaluation benchmark for Arabic LLMs. For showcasing purposes, we train a new suite of models, including a 14 billion parameter model, the largest monolingual Arabic decoder-only model to date. We use a collection of publicly available datasets, as well as a newly introduced HandMade dataset consisting of 8 billion tokens. Finally, we explore the quantitative and qualitative toxicity of several Arabic models, comparing our models to existing public Arabic LLMs.

This paper presents the ArBanking77, a large Arabic dataset for intent detection in the banking domain. Our dataset was arabized and localized from the original English Banking77 dataset, which consists of 13,083 queries to ArBanking77 dataset with 31,404 queries in both Modern Standard Arabic (MSA) and Palestinian dialect, with each query classified into one of the 77 classes (intents). Furthermore, we present a neural model, based on AraBERT, fine-tuned on ArBanking77, which achieved an F1-score of 0.9209 and 0.8995 on MSA and Palestinian dialect, respectively. We performed extensive experimentation in which we simulated low-resource settings, where the model is trained on a subset of the data and augmented with noisy queries to simulate colloquial terms, mistakes and misspellings found in real NLP systems, especially live chat queries. The data and the models are publicly available at https://sina.birzeit.edu/arbanking77.

This paper presents the first Arabic crossword puzzle generator driven by advanced AI technology. Leveraging cutting-edge large language models including GPT4, GPT3-Davinci, GPT3-Curie, GPT3-Babbage, GPT3-Ada, and BERT, the system generates distinctive and challenging clues. Based on a dataset comprising over 50,000 clue-answer pairs, the generator employs fine-tuning, few/zero-shot learning strategies, and rigorous quality-checking protocols to enforce the generation of high-quality clue-answer pairs. Importantly, educational crosswords contribute to enhancing memory, expanding vocabulary, and promoting problem-solving skills, thereby augmenting the learning experience through a fun and engaging approach, reshaping the landscape of traditional learning methods. The overall system can be exploited as a powerful educational tool that amalgamates AI and innovative learning techniques, heralding a transformative era for Arabic crossword puzzles and the intersection of technology and education.

Research studies on Machine Translation (MT) between Modern Standard Arabic (MSA) and English are abundant. However, studies on MT between Omani Arabic (OA) dialects and English are very scarce. This research study focuses on the lack of availability of an Omani dialect parallel dataset, as well as MT of OA to English. The study uses social media data from X (formerly Twitter) to build an authentic parallel text of the Omani dialects. The research presents baseline results on this dataset using Google Translate, Microsoft Translation, and Marian NMT. A taxonomy of the most common linguistic errors is used to analyze the translations made by the NMT systems to provide insights on future improvements. Finally, transfer learning is used to adapt Marian NMT to the Omani dialect, which significantly improved by 9.88 points in the BLEU score.

Traditional NER systems are typically trained to recognize coarse-grained categories of entities, and less attention is given to classifying entities into a hierarchy of fine-grained lower-level sub-types. This article aims to advance Arabic NER with fine-grained entities. We chose to extend Wojood (an open-source Nested Arabic Named Entity Corpus) with sub-types. In particular, four main entity types in Wojood (geopolitical entity (GPE), location (LOC), organization (ORG), and facility (FAC) are extended with 31 sub-types of entities. To do this, we first revised Wojood’s annotations of GPE, LOC, ORG, and FAC to be compatible with the LDC’s ACE guidelines, which yielded 5, 614 changes. Second, all mentions of GPE, LOC, ORG, and FAC (~ 44K) in Wojood are manually annotated with the LDC’s ACE subtypes. This extended version of Wojood is called WojoodFine. To evaluate our annotations, we measured the inter-annotator agreement (IAA) using both Cohen’s Kappa and F1 score, resulting in 0.9861 and 0.9889, respectively. To compute the baselines of WojoodFine, we fine-tune three pre-trained Arabic BERT encoders in three settings: flat NER, nested NER and nested NER with sub-types and achieved F1 score of 0.920, 0.866, and 0.885, respectively. Our corpus and models are open source and available at https://sina.birzeit.edu/wojood/.

Numerous languages exhibit shared characteristics, especially in morphological features. For instance, Arabic and Russian both belong to the fusional language category. The question arises: Do such common traits influence language comprehension across diverse linguistic backgrounds? This study explores the possibility of transferring comprehension skills across languages to Arabic in a zero-shot scenario. Specifically, we demonstrate that training language models on other languages can enhance comprehension of Arabic, as evidenced by our evaluations in three key tasks: natural language inference, question answering, and named entity recognition. Our experiments reveal that certain morphologically rich languages (MRLs), such as Russian, display similarities to Arabic when assessed in a zero-shot context, particularly in tasks like question answering and natural language inference. However, this similarity is less pronounced in tasks like named entity recognition.

Large Language Models (LLMs) such as ChatGPT and Bard AI have gained much attention due to their outstanding performance on a range of NLP tasks. These models have demonstrated remarkable proficiency across various languages without the necessity for full supervision. Nevertheless, their performance in low-resource languages and dialects, like Arabic dialects in comparison to English, remains to be investigated. In this paper, we conduct a comprehensive evaluation of three LLMs for Dialectal Arabic Sentiment Analysis: namely, ChatGPT based on GPT-3.5 and GPT-4, and Bard AI. We use a Saudi dialect Twitter dataset to assess their capability in sentiment text classification and generation. For classification, we compare the performance of fully fine-tuned Arabic BERT-based models with the LLMs in few-shot settings. For data generation, we evaluate the quality of the generated new sentiment samples using human and automatic evaluation methods. The experiments reveal that GPT-4 outperforms GPT-3.5 and Bard AI in sentiment analysis classification, rivaling the top-performing fully supervised BERT-based language model. However, in terms of data generation, compared to manually annotated authentic data, these generative models often fall short in producing high-quality Dialectal Arabic text suitable for sentiment analysis.

Delegating short answer grading to automated systems enhances efficiency, giving teachers more time for vital human-centered aspects of education. Studies in automatic short answer grading (ASAG) approach the problem from instance-based or reference-based perspectives. Recent studies have favored instance-based methods, but they demand substantial data for training, which is often scarce in classroom settings. This study compares both approaches using an Arabic ASAG dataset. We employ in-context meta-learning for instance-based and semantic score-based similarity for reference-based grading. Results show both methods outperform a baseline and occasionally even surpass human raters when grading unseen answers. Notably, the semantic score-based similarity approach excels in zero-shot settings, outperforming in-context meta-learning. Our work contributes insights to Arabic ASAG and introduces a prompt category classification model, leveraging GPT3.5 to augment Arabic data for improved performance.

SALMA, the first Arabic sense-annotated corpus, consists of ~34K tokens, which are all sense-annotated. The corpus is annotated using two different sense inventories simultaneously (Modern and Ghani). SALMA novelty lies in how tokens and senses are associated. Instead of linking a token to only one intended sense, SALMA links a token to multiple senses and provides a score to each sense. A smart web-based annotation tool was developed to support scoring multiple senses against a given word. In addition to sense annotations, we also annotated the corpus using six types of named entities. The quality of our annotations was assessed using various metrics (Kappa, Linear Weighted Kappa, Quadratic Weighted Kappa, Mean Average Error, and Root Mean Square Error), which show very high inter-annotator agreement. To establish a Word Sense Disambiguation baseline using our SALMA corpus, we developed an end-to-end Word Sense Disambiguation system using Target Sense Verification. We used this system to evaluate three Target Sense Verification models available in the literature. Our best model achieved an accuracy with 84.2% using Modern and 78.7% using Ghani. The full corpus and the annotation tool are open-source and publicly available at https://sina.birzeit.edu/salma/.

This paper provides a systematic analysis and comparison of the performance of state-of-the-art models on the task of fine-grained Arabic dialect identification using the MADAR parallel corpus. We test approaches based on pre-trained transformer language models in addition to Naive Bayes models with a rich set of various features. Through a comprehensive data- and error analysis, we provide valuable insights into the strengths and weaknesses of both approaches. We discuss which dialects are more challenging to differentiate, and identify potential sources of errors. Our analysis reveals an important problem with identical sentences across dialect classes in the test set of the MADAR-26 corpus, which may confuse any classifier. We also show that none of the tested approaches captures the subtle distinctions between closely related dialects.

Automatic Arabic Dialect Identification (ADI) of text has gained great popularity since it was introduced in the early 2010s. Multiple datasets were developed, and yearly shared tasks have been running since 2018. However, ADI systems are reported to fail in distinguishing between the micro-dialects of Arabic. We argue that the currently adopted framing of the ADI task as a single-label classification problem is one of the main reasons for that. We highlight the limitation of the incompleteness of the Dialect labels and demonstrate how it impacts the evaluation of ADI systems. A manual error analysis for the predictions of an ADI, performed by 7 native speakers of different Arabic dialects, revealed that ≈ 67% of the validated errors are not true errors. Consequently, we propose framing ADI as a multi-label classification task and give recommendations for designing new ADI datasets.

Most recent models for Arabic topic classification leveraged fine-tuning existing pre-trained transformer models and targeted a limited number of categories. More recently, advances in automated ML and generative models introduced novel potentials for the task. While these approaches work for English, it is a question of whether they perform well for low-resourced languages; Arabic in particular. This paper presents (i) ArBoNeClass; a novel Arabic dataset with an extended 14-topic class set covering modern books from social sciences and humanities along with newspaper articles, and (ii) a set of topic classifiers built from it. We finetuned an open LLM model to build ArGTClass. We compared its performance against the best models built with Vertex AI (Google), AutoML(H2O), and AutoTrain(HuggingFace). ArGTClass outperformed the VertexAi and AutoML models and was reasonably similar to the AutoTrain model.

The remarkable capabilities of Natural Language Models to grasp language subtleties has paved the way for their widespread adoption in diverse fields. However, adapting them for specific tasks requires the time-consuming process of fine-tuning, which consumes significant computational power and energy. Therefore, optimizing the fine-tuning time is advantageous. In this study, we propose an alternate approach that limits parameter manipulation to select layers. Our exploration led to identifying layers that offer the best trade-off between time optimization and performance preservation. We further validated this approach on multiple downstream tasks, and the results demonstrated its potential to reduce fine-tuning time by up to 50% while maintaining performance within a negligible deviation of less than 5%. This research showcases a promising technique for significantly improving fine-tuning efficiency without compromising task- or domain-specific learning capabilities.

Low-resource Machine Translation (MT) is characterized by the scarce availability of training data and/or standardized evaluation benchmarks. In the context of Dialectal Arabic, recent works introduced several evaluation benchmarks covering both Modern Standard Arabic (MSA) and dialects, mapping, however, mostly to a single Indo-European language - English. In this work, we introduce a multi-lingual corpus consisting of 120,600 multi-parallel sentences in English, French, German, Greek, Spanish, and MSA selected from the OpenSubtitles corpus, which were manually translated into the North Levantine Arabic. By conducting a series of training and fine-tuning experiments, we explore how this novel resource can contribute to the research on Arabic MT.

Automatic Text Simplification (TS) involves simplifying language complexity while preserving the original meaning. The main objective of TS is to enhance the readability of complex texts, making them more accessible to a broader range of readers. This work focuses on developing a lexical text simplification system specifically for Arabic. We utilized FastText and Arabert pre-trained embedding models to create various simplification models. Our lexical approach involves a series of steps: identifying complex words, generating potential replacements, and selecting one replacement for the complex word within a sentence. We presented two main identification models: binary and multi-complexity models. We assessed the efficacy of these models by employing BERTScore to measure the similarity between the sentences generated by these models and the intended simple sentences. This comparative analysis evaluated the effectiveness of these models in accurately identifying and selecting complex words.

Detecting offensive language in under-resourced languages presents a significant real-world challenge for social media platforms. This paper is the first work focused on the issue of offensive language detection in Arabizi, an under-explored topic in an under-resourced form of Arabic. For the first time, a comprehensive and critical overview of the existing work on the topic is presented. In addition, we carry out experiments using different BERT-like models and show the feasibility of detecting offensive language in Arabizi with high accuracy. Throughout a thorough analysis of results, we emphasize the complexities introduced by dialect variations and out-of-domain generalization. We use in our experiments a dataset that we have constructed by leveraging existing, albeit limited, resources. To facilitate further research, we make this dataset publicly accessible to the research community.

In this paper, we describe a spoken Arabic dialect identification (ADI) model for Arabic that consistently outperforms previously published results on two benchmark datasets: ADI-5 and ADI-17. We explore two architectural variations: ResNet and ECAPA-TDNN, coupled with two types of acoustic features: MFCCs and features exratected from the pre-trained self-supervised model UniSpeech-SAT Large, as well as a fusion of all four variants. We find that individually, ECAPA-TDNN network outperforms ResNet, and models with UniSpeech-SAT features outperform models with MFCCs by a large margin. Furthermore, a fusion of all four variants consistently outperforms individual models. Our best models outperform previously reported results on both datasets, with accuracies of 84.7% and 96.9% on ADI-5 and ADI-17, respectively.

Arabic is a complex language with many varieties and dialects spoken by ~ 450 millions all around the world. Due to the linguistic diversity and vari-ations, it is challenging to build a robust and gen-eralized ASR system for Arabic. In this work, we address this gap by developing and demoing a system, dubbed VoxArabica, for dialect identi-fication (DID) as well as automatic speech recog-nition (ASR) of Arabic. We train a wide range of models such as HuBERT (DID), Whisper, and XLS-R (ASR) in a supervised setting for Arabic DID and ASR tasks. Our DID models are trained to identify 17 different dialects in addition to MSA. We finetune our ASR models on MSA, Egyptian, Moroccan, and mixed data. Additionally, for the re-maining dialects in ASR, we provide the option to choose various models such as Whisper and MMS in a zero-shot setting. We integrate these models into a single web interface with diverse features such as audio recording, file upload, model selec-tion, and the option to raise flags for incorrect out-puts. Overall, we believe VoxArabica will be use-ful for a wide range of audiences concerned with Arabic research. Our system is currently running at https://cdce-206-12-100-168.ngrok.io/.

This paper outlines the KSAA-RD shared task, which aims to develop a Reverse Dictionary (RD) system for the Arabic language. RDs allow users to find words based on their meanings or definition. This shared task, KSAA-RD, includes two subtasks: Arabic RD and cross-lingual reverse dictionaries (CLRD). Given a definition (referred to as a “gloss”) in either Arabic or English, the teams compete to find the most similar word embeddings of their corresponding word. The winning team achieved 24.20 and 12.70 for RD and CLRD, respectively in terms of rank metric. In this paper, we describe the methods employed by the participating teams and offer an outlook for KSAA-RD.

To extract the ‘meaning’ of a gloss phrase, we build a list of sense-IDs for each word in the phrase which is in our vocabulary. We choose one sense-ID from each list so as to maximise similarity of all the IDs in the chosen subset. We take the meaning of the phrase in semantic space to be the weighted sum of the embedding vectors of the IDs.

A reverse dictionary takes a descriptive phrase of a particular concept and returns words with definitions that align with that phrase. While many reverse dictionaries cater to languages such as English and are readily available online or have been developed by researchers, there is a notable lack of similar resources for the Arabic language. This paper describes our participation in the Arabic Reverse Dictionary shared task. Our proposed method consists of two main steps: First, we convert word definitions into multidimensional vectors. Then, we train these encoded vectors using the Semi-Decoder model for our target task. Our system secured 2nd place based on the Rank metric for both embeddings (Electra and Sgns).

This paper presents a novel approach to the Arabic Reverse Dictionary Shared Task at WANLP 2023 by leveraging the BERT Multilingual model and introducing modifications augmentation and using a multi attention head. The proposed method aims to enhance the performance of the model in understanding and generating word embeddings for Arabic definitions, both in monolingual and cross-lingual contexts. It achieved good results compared to benchmark and other models in the shared task 1 and 2.

A Reverse Dictionary is a tool enabling users to discover a word based on its provided definition, meaning, or description. Such a technique proves valuable in various scenarios, aiding language learners who possess a description of a word without its identity, and benefiting writers seeking precise terminology. These scenarios often encapsulate what is referred to as the “Tip-of-the-Tongue” (TOT) phenomena. In this work, we present our winning solution for the Arabic Reverse Dictionary shared task. This task focuses on deriving a vector representation of an Arabic word from its accompanying description. The shared task encompasses two distinct subtasks: the first involves an Arabic definition as input, while the second employs an English definition. For the first subtask, our approach relies on an ensemble of finetuned Arabic BERT-based models, predicting the word embedding for a given definition. The final representation is obtained through averaging the output embeddings from each model within the ensemble. In contrast, the most effective solution for the second subtask involves translating the English test definitions into Arabic and applying them to the finetuned models originally trained for the first subtask. This straightforward method achieves the highest score across both subtasks.

We present an overview of the ArAIEval shared task, organized as part of the first ArabicNLP 2023 conference co-located with EMNLP 2023. ArAIEval offers two tasks over Arabic text: (1) persuasion technique detection, focusing on identifying persuasion techniques in tweets and news articles, and (2) disinformation detection in binary and multiclass setups over tweets. A total of 20 teams participated in the final evaluation phase, with 14 and 16 teams participating in Task 1 and Task 2, respectively. Across both tasks, we observe that fine-tuning transformer models such as AraBERT is the core of majority of participating systems. We provide a description of the task setup, including description of datasets construction and the evaluation setup. We also provide a brief overview of the participating systems. All datasets and evaluation scripts from the shared task are released to the research community. We hope this will enable further research on such important tasks within the Arabic NLP community.

This paper outlines a methodology aimed at combating disinformation in Arabic social media, a strategy that secured a first-place finish in tasks 2A and 2B at the ArAIEval shared task during the ArabicNLP 2023 conference. Our team, DetectiveRedasers, developed a hyperparameter-optimized pipeline centered around singular BERT-based models for the Arabic language, enhanced by a soft-voting ensemble strategy. Subsequent evaluation on the test dataset reveals that ensembles, although generally resilient, do not always outperform individual models. The primary contributions of this paper are its multifaceted strategy, which led to winning solutions for both binary (2A) and multiclass (2B) disinformation classification tasks.

Propaganda frequently employs sophisticated persuasive strategies in order to influence public opinion and manipulate perceptions. As a result, automating the detection of persuasive techniques is critical in identifying and mitigating propaganda on social media and in mainstream media. This paper proposes a set of transformer-based models for detecting persuasive techniques in tweets and news that incorporate content type information as extra features or as an extra learning objective in a multitask learning setting. In addition to learning to detect the presence of persuasive techniques in text, our best model learns specific syntactic and lexical cues used to express them based on text genre (type) as an auxiliary task. To optimize the model and deal with data imbalance, a focal loss is used. As part of ArabicNLP2023-ArAIEval shared task, this model achieves the highest score in the shared task 1A out of 13 participants, according to the official results, with a micro-F1 of 76.34% and a macro-F1 of 73.21% on the test dataset.

In this research paper, we undertake a comprehensive examination of several pivotal factors that impact the performance of Arabic Disinformation Detection in the ArAIEval’2023 shared task. Our exploration encompasses the influence of surface preprocessing, morphological preprocessing, the FastText vector model, and the weighted fusion of TF-IDF features. To carry out classification tasks, we employ the Linear Support Vector Classification (LSVC) model. In the evaluation phase, our system showcases significant results, achieving an F₁ micro score of 76.70% and 50.46% for binary and multiple classification scenarios, respectively. These accomplishments closely correspond to the average F₁ micro scores achieved by other systems submitted for the second subtask, standing at 77.96% and 64.85% for binary and multiple classification scenarios, respectively.

In this paper, we highlight our approach for the “Arabic AI Tasks Evaluation (ArAiEval) Shared Task 2023”. We present our approaches for task 1-A and task 2-A of the shared task which focus on persuasion technique detection and disinformation detection respectively. Detection of persuasion techniques and disinformation has become imperative to avoid distortion of authentic information. The tasks use multigenre snippets of tweets and news articles for the given binary classification problem. We experiment with several transformer-based models that were pre-trained on the Arabic language. We fine-tune these state-of-the-art models on the provided dataset. Ensembling is employed to enhance the performance of the systems. We achieved a micro F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task 2-A (7th rank on the leaderboard) respectively.

In an era of widespread digital communication, the challenge of identifying and countering disinformation has become increasingly critical. However, compared to the solutions available in the English language, the resources and strategies for tackling this multifaceted problem in Arabic are relatively scarce. To address this issue, this paper presents our solutions to tasks in ArAIEval 2023. Task 1 focuses on detecting persuasion techniques, while Task 2 centers on disinformation detection within Arabic text. Leveraging a multi-head model architecture, fine-tuning techniques, sequential learning, and innovative activation functions, our contributions significantly enhance persuasion techniques and disinformation detection accuracy. Beyond improving performance, our work fills a critical research gap in content analysis for Arabic, empowering individuals, communities, and digital platforms to combat deceptive content effectively and preserve the credibility of information sources within the Arabic-speaking world.

Disinformation involves the dissemination of incomplete, inaccurate, or misleading information; it has the objective, goal, or purpose of deliberately or intentionally lying to others aboutthe truth. The spread of disinformative information on social media has serious implications, and it causes concern among internet users in different aspects. Automatic classification models are required to detect disinformative posts on social media, especially on Twitter. In this article, DistilBERT multilingual model was fine-tuned to classify tweets either as dis-informative or not dis-informative in Subtask 2A of the ArAIEval shared task. The system outperformed the baseline and achieved F1 micro 87% and F1 macro 80%. Our system ranked 11 compared with all participants.

The rapid proliferation of disinformation through social media has become one of the most dangerous means to deceive and influence people’s thoughts, viewpoints, or behaviors due to social media’s facilities, such as rapid access, lower cost, and ease of use. Disinformation can spread through social media in different ways, such as fake news stories, doctored images or videos, deceptive data, and even conspiracy theories, thus making detecting disinformation challenging. This paper is a part of participation in the ArAIEval competition that relates to disinformation detection. This work evaluated four models: MARBERT, the proposed ensemble model, and two tests over GPT-4 (zero-shot and Few-shot). GPT-4 achieved micro-F1 79.01% while the ensemble method obtained 76.83%. Despite no improvement in the micro-F1 score on the dev dataset using the ensemble approach, we still used it for the test dataset predictions. We believed that merging different classifiers might enhance the system’s prediction accuracy.

The rise of propaganda and disinformation in the digital age has necessitated the development of effective detection methods to combat the spread of deceptive information. In this paper we present our approach proposed for ArAIEval shared task : propaganda and disinformation detection in Arabic text. Our system utilised different pre-trained BERT based models, that makes use of prompt-learning based on knowledgeable expansion and prefix-tuning. The proposed approach secured third place in subtask-1A with 0.7555 F1-micro score, second place in subtask-1B with 0.5658 F1-micro score. However, for subtask-2A & 2B, the proposed system achieved fourth place with an F1-micro score of 0.9040, 0.8219 respectively. Our findings suggest that prompt-tuning-based & prefix-tuning based models performed better than conventional fine-tuning. Furthermore, using loss aware class imbalance, improved performance.

Social media has significantly amplified the dissemination of misinformation. Researchers have employed natural language processing and machine learning techniques to identify and categorize false information on these platforms. While there is a well-established body of research on detecting fake news in English and Latin languages, the study of Arabic fake news detection remains limited. This paper describes the methods used to tackle the challenges of the ArAIEval shared Task 2023. We conducted experiments with both monolingual Arabic and multi-lingual pre-trained Language Models (LM). We found that the monolingual Arabic models outperformed in all four subtasks. Additionally, we explored a novel lossless compression method, which, while not surpassing pretrained LM performance, presents an intriguing avenue for future experimentation to achieve comparable results in a more efficient and rapid manner.

To enhance persuasion detection, we investigate the use of multilingual systems on Arabic data by conducting a total of 22 experiments using baselines, multilingual, and monolingual language transformers. Our aim is to provide a comprehensive evaluation of the various systems employed throughout this task, with the ultimate goal of comparing their performance and identifying the most effective approach. Our empirical analysis shows that *ReDASPersuasion* system performs best when combined with multilingual “XLM-RoBERTa” and monolingual pre-trained transformers on Arabic dialects like “CAMeLBERT-DA SA” depending on the NLP classification task.

In this paper, we introduce our participating system to the ArAIEval Shared Task, addressing both the detection of persuasion techniques and disinformation tasks. Our proposed system employs a pre-trained transformer-based language model for Arabic, alongside a classifier. We have assessed the performance of three Arabic Pre-trained Language Models (PLMs) for sentence encoding. Additionally, to enhance our model’s performance, we have explored various training objectives, including Cross-Entropy loss, regularized Mixup loss, asymmetric multi-label loss, and Focal Tversky loss. On the official test set, our system has achieved micro-F1 scores of 0.7515, 0.5666, 0.904, and 0.8333 for Sub-Task 1A, Sub-Task 1B, Sub-Task 2A, and Sub-Task 2B, respectively. Furthermore, our system has secured the 4th, 1st, 3rd, and 2nd positions, respectively, among all participating systems in sub-tasks 1A, 1B, 2A, and 2B of the ArAIEval shared task.

This paper presents the pipeline developed by the AAST-NLP team to address both the persuasion technique detection and disinformation detection shared tasks. The proposed system for all the tasks’ sub-tasks consisted of preprocessing the data and finetuning AraBERT on the given datasets, in addition to several procedures performed for each subtask to adapt to the problems faced in it. The previously described system was used in addition to Dice loss as the loss function for sub-task 1A, which consisted of a binary classification problem. In that sub-task, the system came in eleventh place. We trained the AraBERT for task 1B, which was a multi-label problem with 24 distinct labels, using binary cross-entropy to train a classifier for each label. On that sub-task, the system came in third place. We utilised AraBERT with Dice loss on both subtasks 2A and 2B, ranking second and third among the proposed models for the respective subtasks.

This work explores Arabic disinformation identification, a crucial task in natural language processing, using a state-of-the-art NLP model. We highlight the performance of our system model against baseline models, including multilingual and Arabic-specific ones, and showcase the effectiveness of domain-specific pre-trained models. This work advocates for the adoption of tailored pre-trained models in NLP, emphasizing their significance in understanding diverse languages. By merging advanced NLP techniques with domain-specific pre-training, it advances Arabic disinformation identification.

The spread of disinformation and propagandistic content poses a threat to societal harmony, undermining informed decision-making and trust in reliable sources. Online platforms often serve as breeding grounds for such content, and malicious actors exploit the vulnerabilities of audiences to shape public opinion. Although there have been research efforts aimed at the automatic identification of disinformation and propaganda in social media content, there remain challenges in terms of performance. The ArAIEval shared task aims to further research on these particular issues within the context of the Arabic language. In this paper, we discuss our participation in these shared tasks. We competed in subtasks 1A and 2A, where our submitted system secured positions 9th and 10th, respectively. Our experiments consist of fine-tuning transformer models and using zero- and few-shot learning with GPT-4.

In this work, we present our systems developed for “ArAIEval” shared task of ArabicNLP 2023 (CITATION). We used an mBERT transformer for Subtask 1A, which targets persuasion in Arabic tweets, and we used the MARBERT transformer for Subtask 2A to identify disinformation in Arabic tweets. Our persuasion detection system achieved micro-F1 of 0.745 by surpassing the baseline by 13.2%, and registered a macro-F1 of 0.717 based on leaderboard scores. Similarly, our disinformation system recorded a micro-F1 of 0.816, besting the naïve majority by 6.7%, with a macro-F1 of 0.637. Furthermore, we present our preliminary results on a variety of pre-trained models. In terms of overall ranking, our systems placed 7th out of 16 and 12th out of 17 teams for Subtasks 1A and 2A, respectively.

The widespread dissemination of propaganda and disinformation on both social media and mainstream media platforms has become an urgent concern, attracting the interest of various stakeholders such as government bodies and social media companies. The challenge intensifies when dealing with understudied languages like Arabic. In this paper, we outline our approach for detecting persuasion techniques in Arabic tweets and news article paragraphs. We submitted our system to ArAIEval 2023 Shared Task 1, covering both subtasks. Our main contributions include utilizing GPT-3 to discern tone and potential persuasion techniques in text, exploring various base language models, and employing a multi-task learning approach for the specified subtasks.

In this paper, we share our best performing submission to the Arabic AI Tasks Evaluation Challenge (ArAIEval) at ArabicNLP 2023. Our focus was on Task 1, which involves identifying persuasion techniques in excerpts from tweets and news articles. The persuasion technique in Arabic texts was detected using a training loop with XLM-RoBERTa, a language-agnostic text representation model. This approach proved to be potent, leveraging fine-tuning of a multilingual language model. In our evaluation of the test set, we achieved a micro F1 score of 0.64 for subtask A of the competition.

We describe the findings of the fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023). The objective of NADI is to help advance state-of-the-art Arabic NLP by creating opportunities for teams of researchers to collaboratively compete under standardized conditions. It does so with a focus on Arabic dialects, offering novel datasets and defining subtasks that allow for meaningful comparisons between different approaches. NADI 2023 targeted both dialect identification (Subtask1) and dialect-to-MSA machine translation (Subtask 2 and Subtask 3). A total of 58 unique teams registered for the shared task, of whom 18 teams have participated (with 76 valid submissions during test phase). Among these, 16 teams participated in Subtask 1, 5 participated in Subtask 2, and 3 participated in Subtask 3. The winning teams achieved 87.27 F1 on Subtask 1, 14.76 Bleu in Subtask 2, and 21.10 Bleu in Subtask 3, respectively. Results show that all three subtasks remain challenging, thereby motivating future work in this area. We describe the methods employed by the participating teams and briefly offer an outlook for NADI.

With approximately 400 million speakers worldwide, Arabic ranks as the fifth most-spoken language globally, necessitating advancements in natural language processing. This paper addresses this need by presenting a system description of the approaches employed for the subtasks outlined in the Nuanced Arabic Dialect Identification (NADI) task at EMNLP 2023. For the first subtask, involving closed country-level dialect identification classification, we employ an ensemble of two Arabic language models. Similarly, for the second subtask, focused on closed dialect to Modern Standard Arabic (MSA) machine translation, our approach combines sequence-to-sequence models, all trained on an Arabic-specific dataset. Our team ranks 10th and 3rd on subtask 1 and subtask 2 respectively.

In this paper we present our approach towards Arabic Dialect identification which was part of the The Fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023). We tested several techniques to identify Arabic dialects. We obtained the best result by fine-tuning the pre-trained MARBERTv2 model with a modified training dataset. The training set was expanded by sorting tweets based on dialects, concatenating every two adjacent tweets, and adding them to the original dataset as new tweets. We achieved 82.87 on F1 score and we were at the seventh position among 16 participants.

Our system, submitted to the Nuanced Arabic Dialect Identification (NADI-23), tackles the first sub-task: Closed Country-level dialect identification. In this work, we propose a model that is based on an ensemble of layer-wise fine-tuned BERT-based models. The proposed model ranked fourth out of sixteen submissions, with an F1-macro score of 85.43.

Arabic dialects have extensive global usage owing to their significance and the vast number of Arabic speakers. However, technological progress and globalization are leading to significant transformations within Arabic dialects. They are acquiring new characteristics involving novel vocabulary and integrating of linguistic elements from diverse dialects. Consequently, sentiment analysis of these dialects is becoming more challenging. This study categorizes dialects among 18 countries, as introduced by the Nuanced Arabic Dialect Identification (NADI) shared task competition. Our approach incorporates the utilization of the MARABERT and MARABERT v2 models with a range of methodologies, including a feature extraction process. Our findings reveal that the most effective model is achieved by applying averaging and concatenation to the hidden layers of MARABERT v2, followed by feeding the resulting output into convolutional layers. Furthermore, employing the ensemble method on various methods enhances the model’s performance. Our system secures the 6th position among the top performers in the First subtask, achieving an F1 score of 83.73%.

We present our system designed for Subtask 1 in the shared task NADI on Arabic Dialect Identification, which is part of ArabicNLP 2023. In our approach, we utilized models such as: MARBERT, MARBERTv2 (A) and MARBERTv2 (B). Subsequently, we created a majority voting ensemble of these models. We used MARBERTv2 with different hyperparameters, which significantly improved the overall performance of the ensemble model. In terms of performance, our systems achieved a competitive an F1 score of 84.76. Overall, our system secured the 5th position out of 16 participating teams.

This paper presents the approach of the NLPeople team to the Nuanced Arabic Dialect Identification (NADI) 2023 shared task. Subtask 1 involves identifying the dialect of a source text at the country level. Our approach to Subtask 1 makes use of language-specific language models, a clustering and retrieval method to provide additional context to a target sentence, a fine-tuning strategy which makes use of the provided data from the 2020 and 2021 shared tasks, and finally, ensembling over the predictions of multiple models. Our submission achieves a macro-averaged F1 score of 87.27, ranking 1st among the other participants in the task.

In this paper, we conduct an in-depth analysis of several key factors influencing the performance of Arabic Dialect Identification NADI’2023, with a specific focus on the first subtask involving country-level dialect identification. Our investigation encompasses the effects of surface preprocessing, morphological preprocessing, FastText vector model, and the weighted concatenation of TF-IDF features. For classification purposes, we employ the Linear Support Vector Classification (LSVC) model. During the evaluation phase, our system demonstrates noteworthy results, achieving an F₁ score of 62.51%. This achievement closely aligns with the average F₁ scores attained by other systems submitted for the first subtask, which stands at 72.91%.

Dialect identification systems play a significant role in various fields and applications as in speech and language technologies, facilitating language education, supporting sociolinguistic research, preserving linguistic diversity, enhancing text-to-speech systems. In this paper, we provide our findings and results in NADI 2023 shared task for country-level dialect identification and machine translation (MT) from dialect to MSA. The proposed models achieved an F1-score of 86.18 at the dialect identification task, securing second place in first subtask. Whereas for the machine translation task, the submitted model achieved a BLEU score of 11.37 securing fourth and third place in second and third subtask. The proposed model utilizes parameter efficient training methods which achieves better performance when compared to conventional fine-tuning during the experimentation phase.

This paper presents the methods we developed for the Nuanced Arabic Dialect Identification (NADI) 2023 shared task, specifically targeting the two subtasks focussed on sentence-level machine translation (MT) of text written in any of four Arabic dialects (Egyptian, Emirati, Jordanian and Palestinian) to Modern Standard Arabic (MSA). Our team, UniManc, employed models based on T5: multilingual T5 (mT5), multi-task fine-tuned mT5 (mT0) and AraT5. These models were trained based on two configurations: joint model training for all regional dialects (J-R) and independent model training for every regional dialect (I-R). Based on the results of the official NADI 2023 evaluation, our I-R AraT5 model obtained an overall BLEU score of 14.76, ranking first in the Closed Dialect-to-MSA MT subtask. Moreover, in the Open Dialect-to-MSA MT subtask, our J-R AraT5 model also ranked first, obtaining an overall BLEU score of 21.10.

In this paper, we describe our participation in the NADI2023 shared task for the classification of Arabic dialects in tweets. For training, evaluation, and testing purposes, a primary dataset comprising tweets from 18 Arab countries is provided, along with three older datasets. The main objective is to develop a model capable of classifying tweets from these 18 countries. We outline our approach, which leverages various machine learning models. Our experiments demonstrate that large language models, particularly Arabertv2-Large, Arabertv2-Base, and CAMeLBERT-Mix DID MADAR, consistently outperform traditional methods such as SVM, XGBOOST, Multinomial Naive Bayes, AdaBoost, and Random Forests.

The Helsinki-NLP team participated in the NADI 2023 shared tasks on Arabic dialect translation with seven submissions. We used statistical (SMT) and neural machine translation (NMT) methods and explored character- and subword-based data preprocessing. Our submissions placed second in both tracks. In the open track, our winning submission is a character-level SMT system with additional Modern Standard Arabic language models. In the closed track, our best BLEU scores were obtained with the leave-as-is baseline, a simple copy of the input, and narrowly followed by SMT systems. In both tracks, fine-tuning existing multilingual models such as AraT5 or ByT5 did not yield superior performance compared to SMT.

In this paper, we present our approach for the “Nuanced Arabic Dialect Identification (NADI) Shared Task 2023”. We highlight our methodology for subtask 1 which deals with country-level dialect identification. Recognizing dialects plays an instrumental role in enhancing the performance of various downstream NLP tasks such as speech recognition and translation. The task uses the Twitter dataset (TWT-2023) that encompasses 18 dialects for the multi-class classification problem. Numerous transformer-based models, pre-trained on Arabic language, are employed for identifying country-level dialects. We fine-tune these state-of-the-art models on the provided dataset. Ensembling method is leveraged to yield improved performance of the system. We achieved an F1-score of 76.65 (11th rank on leaderboard) on the test dataset.

In this paper, we present our findings within the context of the NADI-2023 Shared Task (Subtask 2). Our task involves developing a translation model from the Palestinian, Jordanian, Emirati, and Egyptian dialects to Modern Standard Arabic (MSA) using the MADAR parallel corpus, even though it lacks a parallel subset for the Emirati dialect. To address this challenge, we conducted a comparative analysis, evaluating the fine-tuning results of various transformer models using the MADAR corpus as a learning resource. Additionally, we assessed the effectiveness of existing translation tools in achieving our translation objectives. The best model achieved a BLEU score of 11.14% on the dev set and 10.02 on the test set.

Motivated by the need for intelligent question answering (QA) systems on the Holy Qur’an and the success of the first Qur’an Question Answering shared task (Qur’an QA 2022 at OSACT 2022), we have organized the second version at ArabicNLP 2023. The Qur’an QA 2023 is composed of two sub-tasks: the passage retrieval (PR) task and the machine reading comprehension (MRC) task. The main aim of the shared task is to encourage state-of-the-art research on Arabic PR and MRC on the Holy Qur’an. Our shared task has attracted 9 teams to submit 22 runs for the PR task, and 6 teams to submit 17 runs for the MRC task. In this paper, we present an overview of the task and provide an outline of the approaches employed by the participating teams in both sub-tasks.

The Holy Qur’an is central to Islam, influencing around two billion Muslims globally, and is known for its linguistic richness and complexity. This article discusses our involvement in the PR task (Task A) of the Qur’an QA 2023 Shared Task. We used two models: one employing the Sentence Transformer and the other using OpenAI’s embeddings for document retrieval. Both models, equipped with a translation feature, help interpret and understand Arabic language queries by translating them, executing the search, and then reverting the results to Arabic. Our results show that incorporating translation functionalities improves the performance in Arabic Question-Answering systems. The model with translation enhancement performed notably better in all metrics compared to the non-translation model.

The Qur’an holds immense theological and historical significance, and developing a technology-driven solution for answering questions from this sacred text is of paramount importance. This paper presents our approach to task B of Qur’an QA 2023, part of EMNLP 2023, addressing this challenge by proposing a robust method for extracting answers from Qur’anic passages. Leveraging the Qur’anic Reading Comprehension Dataset (QRCD) v1.2, we employ innovative techniques and advanced models to improve the precision and contextuality of answers derived from Qur’anic passages. Our methodology encompasses the utilization of start and end logits, Long Short-Term Memory (LSTM) networks, and fusion mechanisms, contributing to the ongoing dialogue at the intersection of technology and spirituality.

This work addresses the challenges of question answering for vintage texts like the Quran. It introduces two tasks: passage retrieval and reading comprehension. For passage retrieval, it employs unsupervised fine-tuning sentence encoders and supervised multi-task learning. In reading comprehension, it fine-tunes an Electra-based model, demonstrating significant improvements over baseline models. Our best AraElectra model achieves 46.1% partial Average Precision (pAP) on the unseen test set, outperforming the baseline by 23%.

The Qur’an QA 2023 shared task has two sub tasks: Passage Retrieval (PR) task and Machine Reading Comprehension (MRC) task. Our participation in the PR task was to further train several Arabic pre-trained models using a Sentence-Transformers architecture and to ensemble the best performing models. The results of the test set did not reflect the results of the development set. CL-AraBERT achieved the best results, with a 0.124 MAP. We also participate in the MRC task by further fine-tuning the base and large variants of AraBERT using Classical Arabic and Modern Standard Arabic datasets. Base AraBERT achieved the best result with the development set with a partial average precision (pAP) of 0.49, while it achieved 0.5 with the test set. In addition, we applied the ensemble approach of best performing models and post-processing steps to the final results. Our experiments with the development set showed that our proposed model achieved a 0.537 pAP. On the test set, our system obtained a pAP score of 0.49.

In this paper, we present our approach to tackle Qur’an QA 2023 shared tasks A and B. To address the challenge of low-resourced training data, we rely on transfer learning together with a voting ensemble to improve prediction stability across multiple runs. Additionally, we employ different architectures and learning mechanisms for a range of Arabic pre-trained transformer-based models for both tasks. To identify unanswerable questions, we propose using a thresholding mechanism. Our top-performing systems greatly surpass the baseline performance on the hidden split, achieving a MAP score of 25.05% for task A and a partial Average Precision (pAP) of 57.11% for task B.

This paper introduces a comprehensive system designed to address two natural language processing tasks: Passage Retrieval (Task A) and Reading Comprehension (Task B), applied to datasets related to the Holy Qur’an. Task A was treated as a measurement of a textual similarity problem where the system leverages OpenAI’s “text-embedding-ada-002” embedding model to transform textual content into numerical representations, with cosine similarity serving as the proximity metric. Task B focuses on the extraction of answers from Qur’anic passages, employing the Generative Pre-trained Transformer-4 (GPT-4) language model. In Task A, the system is evaluated using the Mean Average Precision (MAP) metric, achieving MAP scores of 0.109438 and 0.06426543057 on the development and test datasets with an optimal similarity threshold set at 0.85. Task B evaluation employs partial Average Precision (pAP), where our system surpasses a baseline whole-passage retriever with pAP scores of 0.470 and 0.5393130538 on the development and test datasets, respectively.

We present WojoodNER-2023, the first Arabic Named Entity Recognition (NER) Shared Task. The primary focus of WojoodNER 2023 is on Arabic NER, offering a novel NER datasets (i.e., Wojood) and the definition of subtasks designed to facilitate meaningful comparisons between different NER approaches. WojoodNER-2023 encompassed two Subtasks: FlatNER and NestedNER. A total of 45 unique teams registered for this shared task, with 11 of them actively participating in the test phase. Specifically, 11 teams participated in FlatNER, while 8 teams tackled NestedNER. The winning team achieved F1 score of 91.96 and 93.73 in FlatNER and NestedNER respectively.

This paper describes our submissions to the WojoodNER shared task organized during the first ArabicNLP conference. We participated in the two proposed sub-tasks of flat and nested Named Entity Recognition (NER). Our systems were ranked first over eight and third over eleven in the Nested NER and Flat NER, respectively. All our primary submissions are based on DiffusionNER models (Shen et al., 2023), where the NER task is formulated as a boundary-denoising diffusion process. Experiments on nested WojoodNER achieves the best results with a micro F1-score of 93.73%. For the flat sub-task, our primary system was the third-best system, with a micro F1-score of 91.92%.

We introduce our systems developed for two subtasks in the shared task “Wojood” on Arabic NER detection, part of ArabicNLP 2023. For Subtask 1, we employ the XLM-R model to predict Flat NER labels for given tokens using a single classifier capable of categorizing all labels. For Subtask 2, we use the XLM-R encoder by building 21 individual classifiers. Each classifier corresponds to a specific label and is designed to determine the presence of its respective label. In terms of performance, our systems achieved competitive micro-F1 scores of 0.83 for Subtask 1 and 0.76 for Subtask 2, according to the leaderboard scores.

Named entity recognition (NER) is one of many challenging tasks in Arabic Natural Language Processing. It is also the base of many critical downstream tasks to help understand the source of major trends and public opinion. In this paper, we will describe our submission in the NER Shared Task of ArabicNLP 2023. We used a simple machine reading comprehension-based technique in the Flat NER Subtask ranking eighth on the leaderboard, while we fine-tuned a language model for the Nested NER Subtask ranking third on the leaderboard.

In this paper, we present our submitted system for the WojoodNER Shared Task, addressing both flat and nested Arabic Named Entity Recognition (NER). Our system is based on a BERT-based multi-task learning model that leverages the existing Arabic Pretrained Language Models (PLMs) to encode the input sentences. To enhance the performance of our model, we have employed a multi-task loss variance penalty and combined several training objectives, including the Cross-Entropy loss, the Dice loss, the Tversky loss, and the Focal loss. Besides, we have studied the performance of three existing Arabic PLMs for sentence encoding. On the official test set, our system has obtained a micro-F1 score of 0.9113 and 0.9303 for Flat (Sub-Task 1) and Nested (Sub-Task 2) NER, respectively. It has been ranked in the 6th and the 2nd positions among all participating systems in Sub-Task 1 and Sub-Task 2, respectively.

This paper presents Arabic named entity recognition models by employing the single-task and the multi-task learning paradigms. The models have been developed using character-based contextualized Embeddings from Language Model (ELMo) in the input layers of the bidirectional long-short term memory networks. The ELMo embeddings are quite capable of learning the morphology and contextual information of the tokens in word sequences. The single-task learning models outperformed the multi-task learning models and achieved micro F1-scores of 0.8751 and 0.8884 for the flat and nested annotations, respectively.

The Wojood Named Entity Recognition (NER) shared task introduces a comprehensive Arabic NER dataset encompassing both flat and nested entity tasks, addressing the challenge of limited Arabic resources. In this paper, we present our team LIPN approach to addressing the two subtasks of WojoodNER SharedTask. We frame NER as a span classification problem. We employ a pretrained language model for token representations and neural network classifiers. We use global decoding for flat NER and a greedy strategy for nested NER. Our model secured the first position in flat NER and the fourth position in nested NER during the competition, with an F-score of 91.96 and 92.45 respectively. Our code is publicly available (https://github.com/niamaelkhbir/LIPN-at-WojoodSharedTask).

Named Entity Recognition (NER) is a crucial task in natural language processing that facilitates the extraction of vital information from text. However, NER for Arabic presents a significant challenge due to the language’s unique characteristics. In this paper, we introduce AraBINDER, our submission to the Wojood NER Shared Task 2023 (ArabicNLP 2023). The shared task comprises two sub-tasks: sub-task 1 focuses on Flat NER, while sub-task 2 centers on Nested NER. We have participated in both sub-tasks. The Bi-Encoder has proven its efficiency for NER in English. We employ AraBINDER (Arabic Bi-Encoder for Named Entity Recognition), which uses the power of two transformer encoders and employs contrastive learning to map candidate text spans and entity types into the same vector representation space. This approach frames NER as a representation learning problem that maximizes the similarity between the vector representations of an entity mention and its type. Our experiments reveal that AraBINDER achieves a micro F-1 score of 0.918 for Flat NER and 0.9 for Nested NER on the Wojood dataset.

Named Entity Recognition (NER) is the task of identifying word-units that correspond to mentions as location, organization, person, or currency. In this shared task we tackle flat-entity classification for Arabic, where for each word-unit a single entity should be identified. To resolve the classification problem we propose StagedNER a novel technique to fine-tuning NER downstream tasks that divides the learning process of a transformer-model into two phases, where a model is tasked to learn sequence tags and then entity tags rather than learn both together simultaneously for an input sequence. We create an ensemble of two base models using this method that yield a score of on the development set and an F1 performance of 90.03% on the validation set and 91.95% on the test set.

This paper contributes to German-English code-switching research. We provide the largest corpus of naturally occurring German-English code-switching, where English is included in German text, and two methods for code-switching identification. The first method is rule-based, using wordlists and morphological processing. We use this method to compile a corpus of 25.6M tweets employing German-English code-switching. In our second method, we continue pretraining of a neural language model on this corpus and classify tokens based on embeddings from this language model. Our systems establish SoTA on our new corpus and an existing German-English code-switching benchmark. In particular, we systematically study code-switching for language-ambiguous words which can only be resolved in context, and morphologically mixed words consisting of both English and German morphemes. We distribute both corpora and systems to the research community.

Code-switching (CS), i.e. mixing different languages in a single sentence, is a common phenomenon in communication and can be challenging in many Natural Language Processing (NLP) settings. Previous studies on CS speech have shown promising results for end-to-end speech translation (ST), but have been limited to offline scenarios and to translation to one of the languages present in the source monolingual transcription). In this paper, we focus on two essential yet unexplored areas for real-world CS speech translation: streaming settings, and translation to a third language (i.e., a language not included in the source). To this end, we extend the Fisher and Miami test and validation datasets to include new targets in Spanish and German. Using this data, we train a model for both offline and streaming ST and we establish baseline results for the two settings mentioned earlier.

Nepali-English code-switching (CS) has been a growing phenomenon in Nepalese society, especially in social media. The code-switching text can be leveraged to understand the socio-linguistic behaviours of the multilingual speakers. Existing studies have attempted to identify the language preference of the multilingual speakers for expressing different emotions using text in different language pairs. In this work, we aim to study the language preference of multilingual Nepali-English CS speakers while expressing sentiment in social media. We create a novel dataset for sentiment analysis using the public Nepali-English code-switched comments in YouTube. After performing the statistical study on the dataset, we find that the proportion of use of Nepali language is higher in negative comments when compared with positive comments, hence concluding the preference for using native language while expressing negative sentiment. Machine learning and transformer-based models are used as the baseline models for the dataset for sentiment classification. The dataset is released publicly.

Recognizing code-switching (CS) speech often presents challenges for an automatic speech recognition system (ASR) due to limited linguistic context in short monolingual segments, resulting in language confusion. To mitigate this issue, language identity (LID) is often integrated into the speech recognition system to provide additional linguistic context. However, previous works predominately focus on extracting language identity from speech signals. We introduce a novel approach to learn language identity from pure text data via a dedicated language identity-language model. Besides, we explore two strategies: LID state fusion and language posterior biasing, to integrate the text-derived language identities into the end-to-end ASR system. By incorporating hypothesized language identities, our ASR system gains crucial contextual cues, effectively capturing language transitions and patterns within code-switched utterances. We conduct speech recognition experiments on the SEAME corpus and demonstrate the effectiveness of our proposed methods. Our results reveal significantly improved transcriptions in code-switching scenarios, underscoring the potential of text-derived LID in enhancing code-switching speech recognition.