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FQuAD: French Question Answering Dataset

FQuAD is the French extractive QA dataset from Illuin Technology, built on the SQuAD 1.1 protocol. University students read a French Wikipedia paragraph, write a question, and select the smallest answer span. Over 60,000 answerable QA pairs, with a runnable Potato config to reproduce or extend it.

About this dataset

FQuAD is a French extractive question answering dataset introduced by d'Hoffschmidt and colleagues at Illuin Technology in 2020. It gives French a native reading-comprehension benchmark rather than a machine-translated SQuAD, so French QA models can be trained and compared on the same span-selection task.

The paragraphs are drawn from French Wikipedia 'quality articles' (Articles de qualité). The team sampled articles, split them into paragraphs of at least 500 characters, and — importantly — split train, development, and test at the article level to avoid leakage between them.

Annotation reused the SQuAD 1.1 guidelines and was carried out by university students on a custom platform. For each paragraph a student wrote three to five questions whose answers are spans of text, and selected the smallest span containing each answer; for development and test questions, two additional answers were collected to capture acceptable span variants. FQuAD 1.0/1.1 contain no unanswerable questions. FQuAD 1.1 totals 62,003 questions (the initial FQuAD 1.0 had 26,108).

The Potato config below reproduces this task: it shows a French Wikipedia paragraph, collects a written question, and captures the smallest answer span. Use it to extend FQuAD, build a domain-specific French QA set, or run the SQuAD-style protocol in another language.

Released
2020 (arXiv, Illuin Technology)
Language
French
Source corpus
French Wikipedia quality articles
QA pairs
62,003 (FQuAD 1.1); 26,108 (FQuAD 1.0)
Answer format
Smallest extractive span
Unanswerable questions
None (SQuAD 1.1 style)
PERORGLOCPERORGLOCDATESelect text to annotate

Configuration Fileconfig.yaml

This Potato config reproduces the annotation task. Save it as config.yaml and run potato start config.yaml to try it.

yaml
# FQuAD - French Question Answering Dataset
# Based on d'Hoffschmidt et al., arXiv 2020
# Paper: https://arxiv.org/abs/2002.06071
# Dataset: https://fquad.illuin.tech/
#
# FQuAD is the French extractive QA dataset from Illuin Technology, built on
# the SQuAD 1.1 protocol. University students read a paragraph from a French
# Wikipedia quality article, write a question whose answer is a span of text,
# and select the smallest span that answers it. FQuAD 1.0/1.1 contain no
# SQuAD-2.0-style unanswerable questions.
#
# Annotation workflow (paper section 3.2):
# 1. Read the French Wikipedia paragraph.
# 2. Write a question whose answer is a span within the paragraph.
# 3. Select the smallest span that contains the answer.
# 4. Repeat until 3-5 questions have been written for the paragraph.

annotation_task_name: "FQuAD - French Extractive QA"
task_dir: "."

data_files:
  - sample-data.json

item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

port: 8000
server_name: localhost

annotation_schemes:
  # Step 1: Write a French question answerable by a span
  - annotation_type: text
    name: question
    description: "Rédigez une question dont la réponse est un passage du texte (Write a question whose answer is a span in the passage)"

  # Step 2: Select the smallest answer span
  - annotation_type: span
    name: answer_span
    description: "Sélectionnez le plus petit passage qui contient la réponse (Select the smallest span containing the answer)"
    labels:
      - "Réponse (Answer)"
    label_colors:
      "Réponse (Answer)": "#3b82f6"

annotation_instructions: |
  You will be shown a paragraph from a French Wikipedia quality article. Following
  the FQuAD data-collection protocol (d'Hoffschmidt et al., 2020), which reuses the
  SQuAD 1.1 guidelines:

  1. Read the paragraph carefully.
  2. Write a question in French whose answer is a span of text within the
     paragraph. Aim for difficult questions rather than trivial word-matching.
  3. Select the smallest span in the paragraph that contains the answer.

  Repeat for several questions per paragraph. Every question must be answerable
  from the paragraph — FQuAD 1.0/1.1 contain no unanswerable questions.

html_layout: |
  <div style="padding: 15px; max-width: 800px; margin: auto;">
    <div style="background: #f0f9ff; border: 1px solid #bae6fd; border-radius: 8px; padding: 16px; margin-bottom: 16px;">
      <strong style="color: #0369a1;">Paragraphe (Passage):</strong>
      <p style="font-size: 16px; line-height: 1.8; margin: 8px 0 0 0;">{{text}}</p>
    </div>
  </div>

allow_all_users: true
instances_per_annotator: 50
annotation_per_instance: 2
allow_skip: true
skip_reason_required: false

Sample Datasample-data.json

json
[
  {
    "id": "fquad_001",
    "text": "La tour Eiffel est une tour de fer puddlé située à Paris, à l'extrémité du parc du Champ-de-Mars. Construite par l'ingénieur Gustave Eiffel et ses collaborateurs, elle a été achevée en 1889 pour l'Exposition universelle. Elle mesure environ 330 mètres de hauteur et constitue le monument le plus visité au monde."
  },
  {
    "id": "fquad_002",
    "text": "Le musée du Louvre est le plus grand musée d'art du monde, situé à Paris sur la rive droite de la Seine. Il abrite notamment La Joconde, un tableau peint par Léonard de Vinci. L'entrée principale du musée se fait par une pyramide de verre inaugurée en 1989."
  }
]

// ... and 2 more items

Get This Design

View on GitHub

Clone or download from the repository

Quick start:

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/text/reading-comprehension/fquad-french-qa
potato start config.yaml

Dataset & paper

d'Hoffschmidt et al., arXiv 2020

Citation (BibTeX)

bibtex
@article{dhoffschmidt2020fquad,
    title = "{FQ}u{AD}: {F}rench Question Answering Dataset",
    author = "d'Hoffschmidt, Martin and Belblidia, Wacim and Brendl{\'e}, Tom and Heinrich, Quentin and Vidal, Maxime",
    journal = "arXiv preprint arXiv:2002.06071",
    year = "2020",
    url = "https://arxiv.org/abs/2002.06071"
}

Details

Annotation Types

textspan

Domain

Natural Language ProcessingQuestion AnsweringMultilingual

Use Cases

Reading ComprehensionExtractive QAFrench NLP

Tags

fquadfrenchquestion-answeringreading-comprehensionsquadextractive-qailluin

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