Papers by Yoann Dupont
Establishing a New State-of-the-Art for French Named Entity Recognition (2020.lrec-1)
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| Challenge: | Named entity recognition (NER) is a task consisting in identifying text spans that denote named entities such as person, location and organization names. |
| Approach: | They manually annotated the French TreeBank with information related to named entities . they sketch the underlying annotation guidelines and provide a few figures about the annotations . |
| Outcome: | The French TreeBank is the main source of morphosyntactic and syntactical annotations for French. |
French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English (2022.acl-long)
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| Challenge: | We introduce 1,679 sentence pairs in French that cover stereotypes in ten types of bias like gender and age. |
| Approach: | They build on the US-centered CrowS-pairs dataset to create a multilingual stereotypes dataset that allows for comparability across languages and cultures. |
| Outcome: | The proposed dataset allows for comparability across languages while characterizing biases that are specific to each country and language. |
CamemBERT: a Tasty French Language Model (2020.acl-main)
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Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric de la Clergerie, Djamé Seddah, Benoît Sagot
| Challenge: | Pretrained language models are now ubiquitous in Natural Language Processing, but their use in other languages is limited. |
| Approach: | They propose to train monolingual Transformer-based model for other languages using web crawled data instead of Wikipedia data and a relatively small web crawl dataset leads to better results. |
| Outcome: | The proposed model performs as well as those obtained using larger datasets. |
Unveiling Strengths and Weaknesses of NLP Systems Based on a Rich Evaluation Corpus: The Case of NER in French (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) is an applicative task for which annotation schemes vary . a lack of robustness of some tools towards textual variation limits evaluation . |
| Approach: | They propose a gold corpus for french annotated with a rich tagset that enables comparison with multiple annotation schemes. |
| Outcome: | The proposed framework enables a fair comparison of NER systems across textual genres and annotation schemes. |
Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts (2024.lrec-main)
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Karen Fort, Laura Alonso Alemany, Luciana Benotti, Julien Bezançon, Claudia Borg, Marthese Borg, Yongjian Chen, Fanny Ducel, Yoann Dupont, Guido Ivetta, Zhijian Li, Margot Mieskes, Marco Naguib, Yuyan Qian, Matteo Radaelli, Wolfgang S. Schmeisser-Nieto, Emma Raimundo Schulz, Thiziri Saci, Sarah Saidi, Javier Torroba Marchante, Shilin Xie, Sergio E. Zanotto, Aurélie Névéol
| Challenge: | Recent studies have identified a gap in the availability of tools and resources to study bias in languages other than English and social contexts outside the north of America. |
| Approach: | They use stereotypes to build a corpus of sentence pairs that cover biases in seven cultural contexts. |
| Outcome: | The proposed resource covers a wide range of languages and cultural settings . it favors sentences that express stereotypes in most bias categories . |