# FQuAD: French Question Answering Dataset

arXiv: https://arxiv.org/abs/2002.06071 · HTML: https://mlsanity.com/paper/2002.06071/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 30 of 58)

- [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://mlsanity.com/paper/2308.16884/) (2023)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval](https://mlsanity.com/paper/2104.12741/) (2021)
- [Enhancing Multilingual LLM Pretraining with Model-Based Data Selection](https://mlsanity.com/paper/2502.10361/) (2025)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://mlsanity.com/paper/2107.07903/) (2021)
- [Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources](https://mlsanity.com/paper/2211.15649/) (2022)
- [AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages](https://mlsanity.com/paper/2305.06897/) (2023)
- [Cedille: A large autoregressive French language model](https://mlsanity.com/paper/2202.03371/) (2022)
- [An Efficient Multilingual Language Model Compression through Vocabulary Trimming](https://mlsanity.com/paper/2305.15020/) (2023)
- [ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment](https://mlsanity.com/paper/2305.14463/) (2023)
- [PQuAD: A Persian Question Answering Dataset](https://mlsanity.com/paper/2202.06219/) (2022)
- [ParaShoot: A Hebrew Question Answering Dataset](https://mlsanity.com/paper/2109.11314/) (2021)
- [ModernBERT or DeBERTaV3? Examining Architecture and Data Influence on Transformer Encoder Models Performance](https://mlsanity.com/paper/2504.08716/) (2025)
- [MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages](https://mlsanity.com/paper/2207.00758/) (2022)
- [A Practical Toolkit for Multilingual Question and Answer Generation](https://mlsanity.com/paper/2305.17416/) (2023)
- [Sentence Extraction-Based Machine Reading Comprehension for Vietnamese](https://mlsanity.com/paper/2105.09043/) (2021)
- [Compositional Generalization in Multilingual Semantic Parsing over Wikidata](https://mlsanity.com/paper/2108.03509/) (2021)
- [PAGnol: An Extra-Large French Generative Model](https://mlsanity.com/paper/2110.08554/) (2021)
- [Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding](https://mlsanity.com/paper/2510.15253/) (2025)
- [On the Usability of Transformers-based models for a French Question-Answering task](https://mlsanity.com/paper/2207.09150/) (2022)
- [Data-Efficient French Language Modeling with CamemBERTa](https://mlsanity.com/paper/2306.01497/) (2023)
- [MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages](https://mlsanity.com/paper/2509.04111/) (2025)
- [FQuAD2.0: French Question Answering and knowing that you know nothing](https://mlsanity.com/paper/2109.13209/) (2021)
- [JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/2202.01764/) (2022)
- [VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension](https://mlsanity.com/paper/2203.11400/) (2022)
- [NorQuAD: Norwegian Question Answering Dataset](https://mlsanity.com/paper/2305.01957/) (2023)
- [UQuAD1.0: Development of an Urdu Question Answering Training Data for Machine Reading Comprehension](https://mlsanity.com/paper/2111.01543/) (2021)
- [Alloprof: a new French question-answer education dataset and its use in an information retrieval case study](https://mlsanity.com/paper/2302.07738/) (2023)
- [The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents](https://mlsanity.com/paper/2304.01412/) (2023)

## Abstract (arXiv)

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/.