Dataset Card for Boolq

Dataset Summary

BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally occurring ---they are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks.

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

Data Instances

default

• Size of downloaded dataset files: 8.77 MB
• Size of the generated dataset: 7.83 MB
• Total amount of disk used: 16.59 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "answer": false,
    "passage": "\"All biomass goes through at least some of these steps: it needs to be grown, collected, dried, fermented, distilled, and burned...",
    "question": "does ethanol take more energy make that produces"
}

Data Fields

The data fields are the same among all splits.

default

• question : a string feature.
• answer : a bool feature.
• passage : a string feature.

Data Splits

name	train	validation
default	9427	3270

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

BoolQ is released under the Creative Commons Share-Alike 3.0 license.

Citation Information

@inproceedings{clark2019boolq,
  title =     {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
  author =    {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
  booktitle = {NAACL},
  year =      {2019},
}

Contributions

Thanks to @lewtun , @lhoestq , @thomwolf , @patrickvonplaten , @albertvillanova for adding this dataset.