Dataset: definite_pronoun_resolution

Dataset Card for "definite_pronoun_resolution"

Table of Contents

Dataset Description

Dataset Summary

Composed by 30 students from one of the author's undergraduate classes. These sentence pairs cover topics ranging from real events (e.g., Iran's plan to attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g., Batman) and purely imaginary situations, largely reflecting the pop culture as perceived by the American kids born in the early 90s. Each annotated example spans four lines: the first line contains the sentence, the second line contains the target pronoun, the third line contains the two candidate antecedents, and the fourth line contains the correct antecedent. If the target pronoun appears more than once in the sentence, its first occurrence is the one to be resolved.

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: 0.22 MB
  • Size of the generated dataset: 0.23 MB
  • Total amount of disk used: 0.45 MB

An example of 'train' looks as follows.

    "candidates": ["coreference resolution", "chunking"],
    "label": 0,
    "pronoun": "it",
    "sentence": "There is currently more work on coreference resolution than on chunking because it is a problem that is still far from being solved."

Data Fields

The data fields are the same among all splits.


  • sentence: a string feature.
  • pronoun: a string feature.
  • candidates: a list of string features.
  • label: a classification label, with possible values including 0 (0), 1 (1).

Data Splits Sample Size

name train test
plain_text 1322 564

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

  title={Resolving complex cases of definite pronouns: the winograd schema challenge},
  author={Rahman, Altaf and Ng, Vincent},
  booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
  organization={Association for Computational Linguistics}

Models trained or fine-tuned on definite_pronoun_resolution

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