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 | |
| languages: | |
| - ja | |
| licenses: | |
| - 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", | |
| "answers_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. | |
| - `answers_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 | |
| TBA | |
| ```bibtex | |
| @article{SkelterLabsInc:2022JaQuAD, | |
| author = {Byunghoon, So and | |
| Kyuhong, Byun and | |
| Kyungwon, Kang and | |
| Seongjin, Cho}, | |
| title = {{JaQuAD}: Japanese Question Answering Dataset for Machine | |
| Reading Comprehension}, | |
| year = 2022, | |
| eid = {arXiv:###}, | |
| pages = {arXiv:###}, | |
| archivePrefix = {arXiv}, | |
| eprint = {###}, | |
| } | |
| ``` | |
| ### Acknowledgements | |
| This work was supported by TPU Research Cloud (TRC) program. For training | |
| models, we used cloud TPUs provided by TRC. We also thanks to anotators who | |
| geernated and labeled JaQuAD | |