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Dataset Card for "quail"

Dataset Summary

QuAIL is a reading comprehension dataset. QuAIL contains 15K multi-choice questions in texts 300-350 tokens long 4 domains (news, user stories, fiction, blogs).QuAIL is balanced and annotated for question types.

Supported Tasks and Leaderboards

More Information Needed


More Information Needed

Dataset Structure

Data Instances


  • Size of downloaded dataset files: 6.11 MB
  • Size of the generated dataset: 28.25 MB
  • Total amount of disk used: 34.36 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

    "answers": ["the cousin is not friendly", "the cousin could have been pretier", "not enough information", "the cousin was too nice"],
    "context": "\"That fall came and I went back to Michigan and the school year went by and summer came and I never really thought about it. I'm...",
    "context_id": "f001",
    "correct_answer_id": 0,
    "domain": "fiction",
    "id": "f001_19",
    "metadata": {
        "author": "Joseph Devon",
        "title": "Black Eyed Susan",
        "url": ""
    "question": "After the events in the text what does the author think about the cousin?",
    "question_id": "19",
    "question_type": "Subsequent_state"

Data Fields

The data fields are the same among all splits.


  • id: a string feature.
  • context_id: a string feature.
  • question_id: a string feature.
  • domain: a string feature.
  • author: a string feature.
  • title: a string feature.
  • url: a string feature.
  • context: a string feature.
  • question: a string feature.
  • question_type: a string feature.
  • answers: a list of string features.
  • correct_answer_id: a int32 feature.

Data Splits

name train challenge validation
quail 10246 556 2164

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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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

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Citation Information

  author    = {Anna Rogers and
               Olga Kovaleva and
               Matthew Downey and
               Anna Rumshisky},
  title     = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite
               Real Tasks},
  booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
               2020, The Thirty-Second Innovative Applications of Artificial Intelligence
               Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
               Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
               February 7-12, 2020},
  pages     = {8722--8731},
  publisher = {{AAAI} Press},
  year      = {2020},
  url       = {},
  timestamp = {Thu, 04 Jun 2020 13:18:48 +0200},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}


Thanks to @sai-prasanna, @ngdodd for adding this dataset.

Update on GitHub
Papers with Code

Models trained or fine-tuned on quail