{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fquad-french-question-answering-dataset","title":"FQuAD: French Question Answering Dataset","arxiv_id":"2002.06071","date":"2020-02-14","proceeding":"Findings of the Association for Computational Linguistics 2020","authors":["Martin d'Hoffschmidt","Wacim Belblidia","Tom Brendlé","Quentin Heinrich","Maxime Vidal"],"abstract":"Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, Reading Comprehension has made significant progress over the past few years. However, most results are reported in English since labeled resources available in other languages, such as French, remain scarce. In the present work, we introduce the French Question Answering Dataset (FQuAD). FQuAD is a French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version. We train a baseline model which achieves an F1 score of 92.2 and an exact match ratio of 82.1 on the test set. In order to track the progress of French Question Answering models we propose a leader-board and we have made the 1.0 version of our dataset freely available at https://illuin-tech.github.io/FQuAD-explorer/.","url_abs":"https://arxiv.org/abs/2002.06071v2","url_pdf":"https://arxiv.org/pdf/2002.06071v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":null,"task_name":"FQuAD"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"machine-reading-comprehension","task_name":"Machine Reading Comprehension"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"test","method_name":"Test"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[{"slug":"fquad","name":"FQuAD","full_name":"French Question Answering Dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-fquad-1","task":"Question Answering","dataset":"FQuAD","model":"CamemBERT-Large","rank_in_archive_order":1,"of":7,"metrics":{"EM":"82.1","F1":"92.2"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-fquad-1","task":"Question Answering","dataset":"FQuAD","model":"XLM-RoBERTa-Large","rank_in_archive_order":2,"of":7,"metrics":{"EM":"79.0","F1":"89.5"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-fquad-1","task":"Question Answering","dataset":"FQuAD","model":"CamemBERT-Base","rank_in_archive_order":3,"of":7,"metrics":{"EM":"78.4","F1":"88.4"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-fquad-1","task":"Question Answering","dataset":"FQuAD","model":"XLM-RoBERTa-Base","rank_in_archive_order":5,"of":7,"metrics":{"EM":"75.3","F1":"85.9"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2002.06071","atlas_url":"https://app.syntology.ai/?focus=2002.06071","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}