Papers by Yoann Dupont

5 papers
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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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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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 .

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