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FQuAD: French Question Answering Dataset

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arxiv 2002.06071 v2 pith:YMEMQISP submitted 2020-02-14 cs.CL cs.AIcs.LG

Martin d'Hoffschmidt , Wacim Belblidia , Tom Brendlé , Quentin Heinrich , Maxime Vidal This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords frenchdatasetansweringfquadquestionversionavailablecomprehension
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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/.

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Cited by 3 Pith papers

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    An adaptive, per-language data filtering and deduplication pipeline produces multilingual LLM pre-training corpora that beat prior public datasets on 11 of 14 evaluated languages, and a 20TB, 1,868 language-script dat...

  2. Finnish SQuAD: A Simple Approach to Machine Translation of Span Annotations

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    A color-marker method for translating span annotations with DeepL yields a high-coverage Finnish SQuAD2.0 whose trained QA models beat prior translated datasets.

  3. CamemBERT 2.0: A Smarter French Language Model Aged to Perfection

    cs.CL 2024-11 conditional novelty 5.0 of 10

    CamemBERTv2 and CamemBERTav2, trained on 275B tokens with an improved tokenizer, outperform prior French encoder models on several downstream tasks.

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