Dataset Card for "quoref"

Dataset Summary

Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard coreferences before selecting the appropriate span(s) in the paragraphs for answering questions.

Supported Tasks

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances


  • Size of downloaded dataset files: 4.84 MB
  • Size of the generated dataset: 47.51 MB
  • Total amount of disk used: 52.36 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

    "answers": {
        "answer_start": [1633],
        "text": ["Frankie"]
    "context": "\"Frankie Bono, a mentally disturbed hitman from Cleveland, comes back to his hometown in New York City during Christmas week to ...",
    "id": "bfc3b34d6b7e73c0bd82a009db12e9ce196b53e6",
    "question": "What is the first name of the person who has until New Year's Eve to perform a hit?",
    "title": "Blast of Silence",
    "url": ""

Data Fields

The data fields are the same among all splits.


  • id: a string feature.
  • question: a string feature.
  • context: a string feature.
  • title: a string feature.
  • url: a string feature.
  • answers: a dictionary feature containing:
    • answer_start: a int32 feature.
    • text: a string feature.

Data Splits Sample Size

name train validation
default 19399 2418

Dataset Creation

Curation Rationale

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Source Data

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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    = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and  Matt Gardner},
      title     = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},
      journal   = {arXiv:1908.05803v2 },
      year      = {2019},


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

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Models trained or fine-tuned on quoref