FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain

Yanis Labrak, Adrien Bazoge, Richard Dufour, Beatrice Daille, Pierre-Antoine Gourraud, Emmanuel Morin, Mickael Rouvier

Abstract
This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s). We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.
Anthology ID:
2022.louhi-1.5
Volume:
Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Alberto Lavelli, Eben Holderness, Antonio Jimeno Yepes, Anne-Lyse Minard, James Pustejovsky, Fabio Rinaldi
Venue:
Louhi
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
41–46
Language:
URL:
https://aclanthology.org/2022.louhi-1.5
DOI:
10.18653/v1/2022.louhi-1.5
Bibkey:
Cite (ACL):
Yanis Labrak, Adrien Bazoge, Richard Dufour, Beatrice Daille, Pierre-Antoine Gourraud, Emmanuel Morin, and Mickael Rouvier. 2022. FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain. In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI), pages 41–46, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain (Labrak et al., Louhi 2022)
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https://preview.aclanthology.org/nschneid-patch-4/2022.louhi-1.5.pdf
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