Datasets:
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- expert-generated
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
pretty_name: OpenBookQA
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: openbookqa
Dataset Card for OpenBookQA
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://allenai.org/data/open-book-qa
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 2.76 MB
- Size of the generated dataset: 2.75 MB
- Total amount of disk used: 5.51 MB
Dataset Summary
OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension. OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject.
Supported Tasks and Leaderboards
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
additional
- Size of downloaded dataset files: 1.38 MB
- Size of the generated dataset: 1.38 MB
- Total amount of disk used: 2.75 MB
An example of 'train' looks as follows.
main
- Size of downloaded dataset files: 1.38 MB
- Size of the generated dataset: 1.38 MB
- Total amount of disk used: 2.75 MB
An example of 'validation' looks as follows.
Data Fields
The data fields are the same among all splits.
additional
id: astringfeature.question_stem: astringfeature.choices: a dictionary feature containing:text: astringfeature.label: astringfeature.
answerKey: astringfeature.
main
id: astringfeature.question_stem: astringfeature.choices: a dictionary feature containing:text: astringfeature.label: astringfeature.
answerKey: astringfeature.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| additional | 4957 | 500 | 500 |
| main | 4957 | 500 | 500 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
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
@inproceedings{OpenBookQA2018,
title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},
author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal},
booktitle={EMNLP},
year={2018}
}
Contributions
Thanks to @thomwolf, @patrickvonplaten, @lewtun for adding this dataset.