ymoslem/acl-abstracts huggingface dataset:
A Benchmark Evaluation of Clinical Named Entity Recognition in French (2024)

Background: Transformer-based language models have shown strong performance on many Natural Language Processing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adapted to different languages and sub-domains through training or fine-tuning on specific corpora while remaining lighter than modern Large Language Models (MLMs). Recently, several MLMs have been released for the biomedical domain in French, and experiments suggest that they outperform standard French counterparts. However, no systematic evaluation comparing all models on the same corpora is available. Objective: This paper presents an evaluation of masked language models for biomedical French on the task of clinical named entity recognition. Material and methods: We evaluate biomedical models CamemBERT-bio and DrBERT and compare them to standard French models CamemBERT, FlauBERT and FrAlBERT as well as multilingual mBERT using three publically available corpora for clinical named entity recognition in French. The evaluation set-up relies on gold-standard corpora as released by the corpus developers. Results: Results suggest that CamemBERT-bio outperforms DrBERT consistently while FlauBERT offers competitive performance and FrAlBERT achieves the lowest carbon footprint. Conclusion: This is the first benchmark evaluation of biomedical masked language models for French clinical entity recognition that compares model performance consistently on nested entity recognition using metrics covering performance and environmental impact.
A Benchmark for Recipe Understanding in Artificial Agents (2024)

This paper introduces a novel benchmark that has been designed as a test bed for evaluating whether artificial agents are able to understand how to perform everyday activities, with a focus on the cooking domain. Understanding how to cook recipes is a highly challenging endeavour due to the underspecified and grounded nature of recipe texts, combined with the fact that recipe execution is a knowledge-intensive and precise activity. The benchmark comprises a corpus of recipes, a procedural semantic representation language of cooking actions, qualitative and quantitative kitchen simulators, and a standardised evaluation procedure. Concretely, the benchmark task consists in mapping a recipe formulated in natural language to a set of cooking actions that is precise enough to be executed in the simulated kitchen and yields the desired dish. To overcome the challenges inherent to recipe execution, this mapping process needs to incorporate reasoning over the recipe text, the state of the simulated kitchen environment, common-sense knowledge, knowledge of the cooking domain, and the action space of a virtual or robotic chef. This benchmark thereby addresses the growing interest in human-centric systems that combine natural language processing and situated reasoning to perform everyday activities.
ABLE: Agency-BeLiefs Embedding to Address Stereotypical Bias through Awareness Instead of Obliviousness (2024)

Natural Language Processing (NLP) models tend to inherit and amplify stereotypical biases present in their training data, leading to harmful societal consequences. Current efforts to rectify these biases typically revolve around making models oblivious to bias, which is at odds with the idea that humans require increased awareness to tackle these biases better. This prompts a fundamental research question: are bias-oblivious models the only viable solution to combat stereotypical biases? This paper answers this question by proposing the Agency-BeLiefs Embedding (ABLE) model, a novel approach that actively encodes stereotypical biases into the embedding space. ABLE draws upon social psychological theory to acquire and represent stereotypical biases in the form of agency and belief scores rather than directly representing stereotyped groups. Our experimental results showcase ABLE`s effectiveness in learning agency and belief stereotypes while preserving the language model`s proficiency. Furthermore, we underscore the practical significance of incorporating stereotypes within the ABLE model by demonstrating its utility in various downstream tasks. Our approach exemplifies the potential benefits of addressing bias through awareness, as opposed to the prevailing approach of mitigating bias through obliviousness.
Abstractive Multi-Video Captioning: Benchmark Dataset Construction and Extensive Evaluation (2024)

This paper introduces a new task, abstractive multi-video captioning, which focuses on abstracting multiple videos with natural language. Unlike conventional video captioning tasks generating a specific caption for a video, our task generates an abstract caption of the shared content in a video group containing multiple videos. To address our task, models must learn to understand each video in detail and have strong abstraction abilities to find commonalities among videos. We construct a benchmark dataset for abstractive multi-video captioning named AbstrActs. AbstrActs contains 13.5k video groups and corresponding abstract captions. As abstractive multi-video captioning models, we explore two approaches: end-to-end and cascade. For evaluation, we proposed a new metric, CocoA, which can evaluate the model performance based on the abstractness of the generated captions. In experiments, we report the impact of the way of combining multiple video features, the overall model architecture, and the number of input videos.
Abstract-level Deductive Reasoning for Pre-trained Language Models (2024)

Pre-trained Language Models have been shown to be able to emulate deductive reasoning in natural language. However, PLMs are easily affected by irrelevant information (e.g., entity) in instance-level proofs when learning deductive reasoning. To address this limitation, we propose an Abstract-level Deductive Reasoner (ADR). ADR is trained to predict the abstract reasoning proof of each sample, which guides PLMs to learn general reasoning patterns rather than instance-level knowledge. Experimental results demonstrate that ADR significantly reduces the impact of PLMs learning instance-level knowledge (over 70{\%}).
A Call for Clarity in Beam Search: How It Works and When It Stops (2024)

Text generation with beam search has proven successful in a wide range of applications. We point out that, though largely overlooked in the literature, the commonly-used implementation of beam decoding (e.g., Hugging Face Transformers and fairseq) uses a first come, first served heuristic: it keeps a set of already completed sequences over time steps and stops when the size of this set reaches the beam size. Based on this finding, we introduce a patience factor, a simple modification to this beam decoding implementation, that generalizes the stopping criterion and provides flexibility to the depth of search. Empirical results demonstrate that adjusting this patience factor improves decoding performance of strong pretrained models on news text summarization and machine translation over diverse language pairs, with a negligible inference slowdown. Our approach only modifies one line of code and can be thus readily incorporated in any implementation. Further, we find that different versions of beam decoding result in large performance differences in summarization, demonstrating the need for clarity in specifying the beam search implementation in research work. Our code will be available upon publication.
A Canonical Form for Flexible Multiword Expressions (2024)

This paper proposes a canonical form for Multiword Expressions (MWEs), in particular for the Dutch language. The canonical form can be enriched with all kinds of annotations that can be used to describe the properties of the MWE and its components. It also introduces the DUCAME (DUtch CAnonical Multiword Expressions) lexical resource with more than 11k MWEs in canonical form. DUCAME is used in MWE-Finder to automatically generate queries for searching for flexible MWEs in large text corpora.
A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation (2024)

Empowered by the large-scale pretrained language models, existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations. However, they are still plagued by the {\ensuremath{<}}b{\ensuremath{>}}hallucination{\ensuremath{<}}/b{\ensuremath{>}} problem, causing unpredictable factual errors in the generated responses. Recently, knowledge-grounded dialogue generation models, that intentionally invoke external knowledge resources to more informative responses, are also proven to be effective in reducing hallucination. Following the idea of getting high-quality knowledge, a few efforts have achieved pretty good performance on this issue. As some inevitable knowledge noises may also lead to hallucinations, it is emergent to investigate the reason and future directions for building noise-tolerant methods in KGD tasks. In this paper, we analyze the causal story behind this problem with counterfactual reasoning methods. Based on the causal effect analysis, we propose a possible solution for alleviating the hallucination in KGD by exploiting the dialogue-knowledge interaction. Experimental results of our example implementation show that this method can reduce hallucination without disrupting other dialogue performance, while keeping adaptive to different generation models. We hope our efforts can support and call for more attention to developing lightweight techniques towards robust and trusty dialogue systems.
Access Control Framework for Language Collections (2024)

This paper introduces the licence-based access control framework developed by the Language Data Commons of Australia (LDaCA) for a range of language collections, with examples given of implementation for significant Indigenous and Australian English collections. Language collections may be curated for many reasons, such as documentation for language revival, for research, security or commercial purposes. Some language collections are created with the intention of being {\textquotedblleft}Open Access{\textquotedblright}; publicly available with no restriction. Other collections require that access be limited to individuals or groups of people, either at the collection level or at the level of individual items, such as a recording. To facilitate access, while respecting the intended access conditions for a collection, or collection items, some form of user identification and authorisation process is typically required. The access control framework described in this paper is based upon descriptions of access conditions in easy-to-read licences which are stored alongside data files in the collections; and is implemented using identity-based authentication and authorisation systems where required. The framework accommodates accessibility needs from unrestricted to extremely limited access, is dynamic, and able to be modified in response to changes in access needs. Storing licences with the data is a significant development in separating language data and access requirements from access infrastructure.
A Challenge Dataset and Effective Models for Conversational Stance Detection (2024)

Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discussions concerning the same specific topic, as naturally transpire in authentic social media interactions. This constraint arises primarily due to the scarcity of datasets that authentically replicate real social media contexts, hindering the research progress of conversational stance detection. In this paper, we introduce a new multi-turn conversation stance detection dataset (called \textbf{MT-CSD}), which encompasses multiple targets for conversational stance detection. To derive stances from this challenging dataset, we propose a global-local attention network (\textbf{GLAN}) to address both long and short-range dependencies inherent in conversational data. Notably, even state-of-the-art stance detection methods, exemplified by GLAN, exhibit an accuracy of only 50.47{\%}, highlighting the persistent challenges in conversational stance detection. Furthermore, our MT-CSD dataset serves as a valuable resource to catalyze advancements in cross-domain stance detection, where a classifier is adapted from a different yet related target. We believe that MT-CSD will contribute to advancing real-world applications of stance detection research. Our source code, data, and models are available at \url{https://github.com/nfq729/MT-CSD}.
A Closer Look at Clustering Bilingual Comparable Corpora (2024)

We study in this paper the problem of clustering comparable corpora, building upon the observation that different types of clusters can be present in such corpora: monolingual clusters comprising documents in a single language, and bilingual or multilingual clusters comprising documents written in different languages. Based on a state-of-the-art deep variant of Kmeans, we propose new clustering models fully adapted to comparable corpora and illustrate their behavior on several bilingual collections (in English, French, German and Russian) created from Wikipedia.
AcnEmpathize: A Dataset for Understanding Empathy in Dermatology Conversations (2024)

Empathy is critical for effective communication and mental health support, and in many online health communities people anonymously engage in conversations to seek and provide empathetic support. The ability to automatically recognize and detect empathy contributes to the understanding of human emotions expressed in text, therefore advancing natural language understanding across various domains. Existing empathy and mental health-related corpora focus on broader contexts and lack domain specificity, but similarly to other tasks (e.g., learning distinct patterns associated with COVID-19 versus skin allergies in clinical notes), observing empathy within different domains is crucial to providing tailored support. To address this need, we introduce AcnEmpathize, a dataset that captures empathy expressed in acne-related discussions from forum posts focused on its emotional and psychological effects. We find that transformer-based models trained on our dataset demonstrate excellent performance at empathy classification. Our dataset is publicly released to facilitate analysis of domain-specific empathy in online conversations and advance research in this challenging and intriguing domain.
A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances (2024)

Automatic measures of similarity between sentences or utterances are invaluable for training speech synthesizers, evaluating machine translation, and assessing learner productions. While there exist measures for semantic similarity and prosodic similarity, there are as yet none for pragmatic similarity. To enable the training of such measures, we developed the first collection of human judgments of pragmatic similarity between utterance pairs. 9 judges listened to 220 utterance pairs, each consisting of an utterance extracted from a recorded dialog and a re-enactment of that utterance under various conditions designed to create various degrees of similarity. Each pair was rated on a continuous scale. The average inter-judge correlation was 0.45. We make this data available at https://github.com/divettemarco/PragSim .
A Community-Driven Data-to-Text Platform for Football Match Summaries (2024)

Data-to-text systems offer a transformative approach to generating textual content in data-rich environments. This paper describes the architecture and deployment of Prosebot, a community-driven data-to-text platform tailored for generating textual summaries of football matches derived from match statistics. The system enhances the visibility of lower-tier matches, traditionally accessible only through data tables. Prosebot uses a template-based Natural Language Generation (NLG) module to generate initial drafts, which are subsequently refined by the reading community. Comprehensive evaluations, encompassing both human-mediated and automated assessments, were conducted to assess the system`s efficacy. Analysis of the community-edited texts reveals that significant segments of the initial automated drafts are retained, suggesting their high quality and acceptance by the collaborators. Preliminary surveys conducted among platform users highlight a predominantly positive reception within the community.
A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking the Privacy-Utility Trade-off (2024)

The application of Differential Privacy to Natural Language Processing techniques has emerged in relevance in recent years, with an increasing number of studies published in established NLP outlets. In particular, the adaptation of Differential Privacy for use in NLP tasks has first focused on the *word-level*, where calibrated noise is added to word embedding vectors to achieve {\textquotedblleft}noisy{\textquotedblright} representations. To this end, several implementations have appeared in the literature, each presenting an alternative method of achieving word-level Differential Privacy. Although each of these includes its own evaluation, no comparative analysis has been performed to investigate the performance of such methods relative to each other. In this work, we conduct such an analysis, comparing seven different algorithms on two NLP tasks with varying hyperparameters, including the *epsilon* parameter, or privacy budget. In addition, we provide an in-depth analysis of the results with a focus on the privacy-utility trade-off, as well as open-source our implementation code for further reproduction. As a result of our analysis, we give insight into the benefits and challenges of word-level Differential Privacy, and accordingly, we suggest concrete steps forward for the research field.
A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation (2024)

While extensive work has examined the explicit and implicit biases in large language models (LLMs), little research explores the relation between these two types of biases. This paper presents a comparative study of the explicit and implicit biases in LLMs grounded in social psychology. Social psychology distinguishes between explicit and implicit biases by whether the bias can be self-recognized by individuals. Aligning with this conceptualization, we propose a self-evaluation-based two-stage measurement of explicit and implicit biases within LLMs. First, the LLM is prompted to automatically fill templates with social targets to measure implicit bias toward these targets, where the bias is less likely to be self-recognized by the LLM. Then, the LLM is prompted to self-evaluate the templates filled by itself to measure explicit bias toward the same targets, where the bias is more likely to be self-recognized by the LLM. Experiments conducted on state-of-the-art LLMs reveal human-like inconsistency between explicit and implicit occupational gender biases. This work bridges a critical gap where prior studies concentrate solely on either explicit or implicit bias. We advocate that future work highlight the relation between explicit and implicit biases in LLMs.
A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media (2024)

Dehumanisation involves the perception and/or treatment of a social group`s members as less than human. This phenomenon is rarely addressed with computational linguistic techniques. We adapt a recently proposed approach for English, making it easier to transfer to other languages and to evaluate, introducing a new sentiment resource, the use of zero-shot cross-lingual valence and arousal detection, and a new method for statistical significance testing. We then apply it to study attitudes to migration expressed in Slovene newspapers, to examine changes in the Slovene discourse on migration between the 2015-16 migration crisis following the war in Syria and the 2022-23 period following the war in Ukraine. We find that while this discourse became more negative and more intense over time, it is less dehumanising when specifically addressing Ukrainian migrants compared to others.
A Computational Approach to Quantifying Grammaticization of English Deverbal Prepositions (2024)

This paper explores grammaticization of deverbal prepositions by a computational approach based on corpus data. Deverbal prepositions are words or phrases that are derived from a verb and that behave as a preposition such as {\textquotedblleft}regarding{\textquotedblright} and {\textquotedblleft}according to{\textquotedblright}. Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions. This paper augments them by methods for measuring the degree of grammaticization of deverbal prepositions based on non-contextualized or contextualized word vectors. Experiments show that the methods correlate well with human judgements (as high as 0.69 in Spearman`s rank correlation coefficient). Using the best-performing method, this paper further shows that the methods support previous findings in linguistics including (i) Deverbal prepositions are marginal in terms of prepositionality; and (ii) The process where verbs are grammaticized into prepositions is gradual. As a pilot study, it also conducts a diachronic analysis of grammaticization of deverbal preposition.
A Computational Model of Latvian Morphology (2024)

In this paper we describe a computational model of Latvian morphology that provides a formal structure for Latvian word form inflection and has been implemented in software for generation, analysis and lemmatization of Latvian word forms. The work was motivated by the need for a NLP inflection model that can cover all the complexity of Latvian language and explicitly enumerate and handle the many exceptions to the general Latvian inflection principles. This is an evolution of earlier work, extending the initial proof of concept model to properly cover Latvian language. We provide a set of morphological paradigms that differ from current linguistic tradition, a set of systematic stem changes and combine it with an extensive lexicon that includes paradigm information and structured morphological attributes for 118 000 lexemes. This model has been applied on both dictionary and corpora data, demonstrating that it provides a good coverage for modern Latvian literary language. We also consider that there is a good potential to extend this also to the related Latgalian language.
A Concept Based Approach for Translation of Medical Dialogues into Pictographs (2024)

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