Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
Japanese
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
Tags:
License:
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](#table-of-contents) | |
- [Dataset Description](#dataset-description) | |
- [Dataset Summary](#dataset-summary) | |
- [Supported Tasks](#supported-tasks-and-leaderboards) | |
- [Languages](#languages) | |
- [Dataset Structure](#dataset-structure) | |
- [Data Instances](#data-instances) | |
- [Data Fields](#data-fields) | |
- [Data Splitting](#data-splitting) | |
- [Dataset Creation](#dataset-creation) | |
- [Curation Rationale](#curation-rationale) | |
- [Source Data](#source-data) | |
- [Annotations](#annotations) | |
- [Personal and Sensitive Information](#personal-and-sensitive-information) | |
- [Considerations for Using the Data](#considerations-for-using-the-data) | |
- [Social Impact of Dataset](#social-impact-of-dataset) | |
- [Discussion of Biases](#discussion-of-biases) | |
- [Other Known Limitations](#other-known-limitations) | |
- [Additional Information](#additional-information) | |
- [Dataset Curators](#dataset-curators) | |
- [Licensing Information](#licensing-information) | |
- [Citation Information](#citation-information) | |
- [Acknowledgements](#acknowledgements) | |
## Dataset Description | |
- **Repository:** https://github.com/SkelterLabsInc/JaQuAD | |
- **Paper:** [JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension]() | |
- **Point of Contact:** [jaquad@skelterlabs.com](jaquad@skelterlabs.com) | |
- **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](https://huggingface.co/cl-tohoku/bert-base-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': | |
```python | |
{ | |
"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](https://skelterlabs.com/) 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](https://ja.wikipedia.org/). | |
88.7% of articles are from the curated list of Japanese high-quality Wikipedia | |
articles, e.g., [featured articles](https://ja.wikipedia.org/wiki/Wikipedia:%E8%89%AF%E8%B3%AA%E3%81%AA%E8%A8%98%E4%BA%8B) | |
and [good articles](https://ja.wikipedia.org/wiki/Wikipedia:%E7%A7%80%E9%80%B8%E3%81%AA%E8%A8%98%E4%BA%8B). | |
### 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/](https://skelterlabs.com/) | |
### Licensing Information | |
The JaQuAD dataset is licensed under the [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license. | |
### Citation Information | |
```bibtex | |
@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](https://sites.research.google/trc/). | |
For training models, we used cloud TPUs provided by TRC. We also thank | |
annotators who generated JaQuAD. | |