README.md

metadata

annotations_creators:
  - crowdsourced
  - expert-generated
language_creators:
  - expert-generated
language:
  - en
license:
  - 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 Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• 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
  • Contributions

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

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

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 'train' looks as follows:

{'id': '7-980',
 'question_stem': 'The sun is responsible for',
 'choices': {'text': ['puppies learning new tricks',
   'children growing up and getting old',
   'flowers wilting in a vase',
   'plants sprouting, blooming and wilting'],
  'label': ['A', 'B', 'C', 'D']},
 'answerKey': 'D'}

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:

{'id': '7-980',
 'question_stem': 'The sun is responsible for',
 'choices': {'text': ['puppies learning new tricks',
   'children growing up and getting old',
   'flowers wilting in a vase',
   'plants sprouting, blooming and wilting'],
  'label': ['A', 'B', 'C', 'D']},
 'answerKey': 'D',
 'fact1': 'the sun is the source of energy for physical cycles on Earth',
 'humanScore': 1.0,
 'clarity': 2.0,
 'turkIdAnonymized': 'b356d338b7'}

Data Fields

The data fields are the same among all splits.

main

• id: a string feature.
• question_stem: a string feature.
• choices: a dictionary feature containing:
  • text: a string feature.
  • label: a string feature.
• answerKey: a string feature.

additional

• id: a string feature.
• question_stem: a string feature.
• choices: a dictionary feature containing:
  • text: a string feature.
  • label: a string feature.
• answerKey: a string feature.
• fact1 (str): oOriginating common knowledge core fact associated to the question.
• humanScore (float): Human accuracy score.
• clarity (float): Clarity score.
• turkIdAnonymized (str): Anonymized crowd-worker ID.

Data Splits

name	train	validation	test
main	4957	500	500
additional	4957	500	500

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

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.