README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
  - found
language:
  - ja
license:
  - cc-by-sa-3.0
multilinguality:
  - monolingual
paperswithcode_id: null
pretty_name: 'JaQuAD: Japanese Question Answering Dataset'
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - extractive-qa

Dataset Card for JaQuAD

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splitting
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Acknowledgements

Dataset Description

• Repository: https://github.com/SkelterLabsInc/JaQuAD
• Paper: JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension
• Point of Contact: [email protected]
• Size of dataset files: 24.6 MB
• Size of the generated dataset: 48.6 MB
• Total amount of disk used: 73.2 MB

Dataset Summary

Japanese Question Answering Dataset (JaQuAD), released in 2022, is a human-annotated dataset created for Japanese Machine Reading Comprehension. JaQuAD is developed to provide a SQuAD-like QA dataset in Japanese. JaQuAD contains 39,696 question-answer pairs. Questions and answers are manually curated by human annotators. Contexts are collected from Japanese Wikipedia articles. Fine-tuning BERT-Japanese on JaQuAD achieves 78.92% for an F1 score and 63.38% for an exact match.

Supported Tasks

• extractive-qa: This dataset is intended to be used for extractive-qa.

Languages

Japanese (ja)

Dataset Structure

Data Instances

• Size of dataset files: 24.6 MB
• Size of the generated dataset: 48.6 MB
• Total amount of disk used: 73.2 MB

An example of 'validation':

{
    "id": "de-001-00-000",
    "title": "イタセンパラ",
    "context": "イタセンパラ(板鮮腹、Acheilognathuslongipinnis)は、コイ科のタナゴ亜科タナゴ属に分類される淡水>魚の一種。\n別名はビワタナゴ(琵琶鱮、琵琶鰱)。",
    "question": "ビワタナゴの正式名称は何?",
    "question_type": "Multiple sentence reasoning",
    "answers": {
        "text": "イタセンパラ",
        "answer_start": 0,
        "answer_type": "Object",
    },
},

Data Fields

• id: a string feature.
• title: a string feature.
• context: a string feature.
• question: a string feature.
• question_type: a string feature.
• answers: a dictionary feature containing:
  • text: a string feature.
  • answer_start: a int32 feature.
  • answer_type: a string feature.

Data Splitting

JaQuAD consists of three sets, train, validation, and test. They were created from disjoint sets of Wikipedia articles. The test set is not publicly released yet. The following table shows statistics for each set.

Set	Number of Articles	Number of Contexts	Number of Questions
Train	691	9713	31748
Validation	101	1431	3939
Test	109	1479	4009

Dataset Creation

Curation Rationale

The JaQuAD dataset was created by Skelter Labs to provide a SQuAD-like QA dataset in Japanese. Questions are original and based on Japanese Wikipedia articles.

Source Data

The articles used for the contexts are from Japanese Wikipedia. 88.7% of articles are from the curated list of Japanese high-quality Wikipedia articles, e.g., featured articles and good articles.

Annotations

Wikipedia articles were scrapped and divided into one more multiple paragraphs as contexts. Annotations (questions and answer spans) are written by fluent Japanese speakers, including natives and non-natives. Annotators were given a context and asked to generate non-trivial questions about information in the context.

Personal and Sensitive Information

No personal or sensitive information is included in this dataset. Dataset annotators has been manually verified it.

Considerations for Using the Data

Users should consider that the articles are sampled from Wikipedia articles but not representative of all Wikipedia articles.

Social Impact of Dataset

The social biases of this dataset have not yet been investigated.

Discussion of Biases

The social biases of this dataset have not yet been investigated. Articles and questions have been selected for quality and diversity.

Other Known Limitations

The JaQuAD dataset has limitations as follows:

• Most of them are short answers.
• Assume that a question is answerable using the corresponding context.

This dataset is incomplete yet. If you find any errors in JaQuAD, please contact us.

Additional Information

Dataset Curators

Skelter Labs: https://skelterlabs.com/

Licensing Information

The JaQuAD dataset is licensed under the CC BY-SA 3.0 license.

Citation Information

@misc{so2022jaquad,
      title={{JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension}},
      author={ByungHoon So and Kyuhong Byun and Kyungwon Kang and Seongjin Cho},
      year={2022},
      eprint={2202.01764},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Acknowledgements

This work was supported by TPU Research Cloud (TRC) program. For training models, we used cloud TPUs provided by TRC. We also thank annotators who generated JaQuAD.