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

Languages

More Information Needed

Dataset Structure

Data Instances

quail

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": "http://manybooks.net/pages/devonjother08black_eyed_susan/0.html"
    },
    "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.

quail

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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Annotations

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

More Information Needed

Licensing Information

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Citation Information

@inproceedings{DBLP:conf/aaai/RogersKDR20,
  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       = {https://aaai.org/ojs/index.php/AAAI/article/view/6398},
  timestamp = {Thu, 04 Jun 2020 13:18:48 +0200},
  biburl    = {https://dblp.org/rec/conf/aaai/RogersKDR20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}