Papers by Richard Dufour
A Multimodal Educational Corpus of Oral Courses: Annotation, Analysis and Case Study (2020.lrec-1)
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Salima Mdhaffar, Yannick Estève, Antoine Laurent, Nicolas Hernandez, Richard Dufour, Delphine Charlet, Geraldine Damnati, Solen Quiniou, Nathalie Camelin
| Challenge: | a corpus of spontaneous speech is being developed for educational use . the dataset will be freely available to the research community . |
| Approach: | They propose to use a French speech educational corpus to explore synchronous speech transcription and application in teaching situations. |
| Outcome: | The proposed corpus includes 10 hours of lectures, manually transcribed and segmented . the dataset will be freely available to the research community . |
CASIMIR: A Corpus of Scientific Articles Enhanced with Multiple Author-Integrated Revisions (2024.lrec-main)
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| Challenge: | CASIMIR dataset contains multiple revisions of 15,646 scientific articles . authors question the relevance of current evaluation methods for text revision . |
| Approach: | They propose a textual resource on the revision step of the writing process of scientific articles. |
| Outcome: | The proposed dataset contains the multiple revised versions of 15,646 scientific articles from OpenReview, along with their peer reviews. |
ACL-rlg: A Dataset for Reading List Generation (2025.coling-main)
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| Challenge: | Existing tools for searching the literature return an overwhelming number of results, making familiarization process daunting and inefficient. |
| Approach: | They propose to use ACL-rlg as the largest open expert-annotated reading list dataset to help researchers navigate key literature. |
| Outcome: | The proposed dataset outperforms existing search engines and indexing methods and shows signs of data contamination. |
Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset (2023.emnlp-main)
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| Challenge: | Existing models for named entity recognition have limited range when applied to long documents . lack of supervision for such a task means one has to settle for unsupervised approaches. |
| Approach: | They propose to train a neural context retrieval model based on an instruction-tuned large language model. |
| Outcome: | The proposed method outperforms baselines on an English literary dataset . pre-trained transformer-based models can perform named entity recognition (NER) with great accuracy, but limited range when applied to long documents such as whole novels. |
Coupling Local Context and Global Semantic Prototypes via a Hierarchical Architecture for Rhetorical Roles Labeling (2026.eacl-long)
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Anas Belfathi, Nicolas Hernandez, Monceaux Laura, Warren Bonnard, Mary Catherine Lavissière, Christine Jacquin, Richard Dufour
| Challenge: | Hierarchical models capture local dependencies but lack global, corpus-level representations. |
| Approach: | They propose two prototype-based methods that integrate local context with global representations to address this limitation. |
| Outcome: | The proposed methods integrate local context with global representations. |
A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have enabled advances in the field of natural language processing . however, their application and potential are still underexplored . |
| Approach: | They evaluate four state-of-the-art instruction-tuned Large Language Models on 13 NLP tasks in English. |
| Outcome: | The evaluated models outperform state-of-the-art models on 13 real-world clinical and biomedical NLP tasks in English. |
DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain (2024.lrec-main)
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Yanis Labrak, Adrien Bazoge, Oumaima El Khettari, Mickael Rouvier, Pacome Constant Dit Beaufils, Natalia Grabar, Béatrice Daille, Solen Quiniou, Emmanuel Morin, Pierre-Antoine Gourraud, Richard Dufour
| Challenge: | Existing benchmarks for pre-trained language models are limited to only a few languages . a limited number of tasks are evaluated on non-standardized protocols . |
| Approach: | They propose to aggregate diverse downstream tasks into a benchmark to assess PLMs' qualities . they evaluate 8 pre-trained masked language models on general and biomedical-specific data . |
| Outcome: | The proposed benchmark assesses pre-trained language models on 20 diversified tasks. |
Towards Reliable Paper Contributions Annotation in the ACL Rolling Review (2026.findings-acl)
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| Challenge: | Identifying the types of contributions an article makes can help readers grasp its significance. |
| Approach: | They propose to use a typology to categorize articles by their contributions to improve review quality and fairness. |
| Outcome: | The ACL Rolling Review (ARR) introduced a typology requiring authors to specify their contributions to improve review quality and fairness. |
Identifying Reliable Evaluation Metrics for Scientific Text Revision (2025.acl-long)
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| Challenge: | Effective revision is a critical step in scientific writing, ensuring clarity, coherence, and adherence to academic standards. |
| Approach: | They propose to use ROUGE and BERTScore to assess revision quality . they also examine LLM-as-a-judge approaches to assess instruction-following revisions . |
| Outcome: | The proposed method improves the accuracy of revision tasks with and without a gold reference. |
WAC: A Corpus of Wikipedia Conversations for Online Abuse Detection (2020.lrec-1)
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| Challenge: | Existing methods for moderation of abusive content are limited by the lack of large corpora of conversations. |
| Approach: | They propose a framework with comment-level abuse annotations based on the Wikipedia Comment corpus . they propose 'context-based' approaches to detect abusive content based upon conversational context . |
| Outcome: | The proposed framework can be used to improve the moderation process of abusive content on the Internet. |
The Role of Global and Local Context in Named Entity Recognition (2023.acl-short)
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| Challenge: | Named Entity Recognition (NER) models are usually applied sequentially because of their complexity. |
| Approach: | They explore the impact of global document context on Named Entity Recognition . they find that correctly retrieving global document contextual has a greater impact . |
| Outcome: | The proposed model can retrieve global context better than leveraging local context . authors say the model can push the state of the art further . |
Persistent Homology of Topic Networks for the Prediction of Reader Curiosity (2025.acl-long)
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Manuel D.s. Hopp, Vincent Labatut, Arthur Amalvy, Richard Dufour, Hannah Stone, Hayley K Jach, Kou Murayama
| Challenge: | Existing approaches to model reader engagement rely on surface-level characteristics and often fail to capture the broader semantic structure, narrative flow, and information gaps that stimulate curiosity. |
| Approach: | They propose a framework that quantifies semantic information gaps within a text's semantic structure by using BERTopic-inspired topic modeling and persistent homology to analyze the evolving topology of a dynamic semantic network derived from text segments. |
| Outcome: | The proposed method significantly improves curiosity prediction compared to baseline models (73% vs. 30% explained deviance) |
How Important Is Tokenization in French Medical Masked Language Models? (2024.lrec-main)
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| Challenge: | Word tokenization into subword units has become the prevailing standard in the field of natural language processing (NLP) over recent years . the precise factors contributing to its success remain unclear . |
| Approach: | They propose a tokenization strategy that integrates morpheme-enriched word segmentation into existing tokenization methods. |
| Outcome: | The proposed tokenization strategy outperforms character and word tokenization but the precise factors contributing to its success remain unclear. |
DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains (2023.acl-long)
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Yanis Labrak, Adrien Bazoge, Richard Dufour, Mickael Rouvier, Emmanuel Morin, Béatrice Daille, Pierre-Antoine Gourraud
| Challenge: | Recent studies have shown that pre-trained language models improve performance on a wide range of NLP tasks. |
| Approach: | They propose to use pre-trained language models to train medical domains on French language to compare performance with specialized ones. |
| Outcome: | The proposed models can take advantage of existing biomedical models in a foreign language by further pre-training them on our targeted data. |
A Benchmark of French ASR Systems Based on Error Severity (2025.coling-main)
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| Challenge: | Automatic Speech Recognition (ASR) transcription errors are often assessed using metrics that compare them with a reference transcription. |
| Approach: | They propose to categorize transcription errors into four levels of severity based on objective linguistic criteria, contextual patterns, and the use of content words as the unit of analysis. |
| Outcome: | The proposed evaluation categorizes errors into four levels of severity based on objective linguistic criteria, contextual patterns, and the use of content words as the unit of analysis. |
BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains (2024.findings-acl)
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Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, Richard Dufour
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. |
| Approach: | They propose an open-source LLM tailored for the biomedical domain that utilizes Mistral as its foundation model and pre-trained on PubMed Central. |
| Outcome: | The proposed model outperforms existing models on a benchmark comprising 10 established medical question-answering tasks in English and is competitive with proprietary models. |
The Role of Natural Language Processing Tasks in Automatic Literary Character Network Construction (2025.coling-main)
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| Challenge: | low-level tasks are used to extract character networks from literary texts, but no study has been conducted on their impact on performance. |
| Approach: | They focus on the role of named entity recognition (NER) and coreference resolution when extracting co-occurrence networks. |
| Outcome: | The proposed methods outperform traditional pipelines in terms of recall and recall. |