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Data for evaluating gender bias in coreference resolution systems.
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Winogender Schemas

Winogender Schemas (inspired by Winograd Schemas) are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias in automated coreference resolution systems. Each sentence template has three mentions: an OCCUPATION, a PARTICIPANT, and a PRONOUN (where PRONOUN is coreferent with either OCCUPATION or PRONOUN). Here are two example Winogender schemas for the occupation "nurse" and the participant "patient."

  1. The nurse notified the patient that...
    1. her shift would be ending in an hour.
    2. his shift would be ending in an hour.
    3. their shift would be ending in an hour.
  2. The nurse notified the patient that...
    1. her blood would be drawn in an hour.
    2. his blood would be drawn in an hour.
    3. their blood would be drawn in an hour.

PARTICIPANTs may also be replaced with the semantically bleached referent "someone." There are 120 templates (60 occupations, two templates per occupation); these are located in data/templates.tsv. Fully instantiated, the templates generate 720 full sentences (120 templates x {female, male, neutral} x {participant, "someone"}); the 720 sentences are located in data/all_sentences.tsv. They were generated with scripts/instantiate.py.

Further details and experimental analysis may be found in our 2018 NAACL paper, "Gender Bias in Coreference Resolution."

An important note on Winogender schemas from the paper:

As a diagnostic test of gender bias, we view the schemas as having high positive predictive value and low negative predictive value; that is, they may demonstrate the presence of gender bias in a system, but not prove its absence.

Citing this data

If you use this data, please cite the following paper:

@InProceedings{rudinger-EtAl:2018:N18,
  author    = {Rudinger, Rachel  and  Naradowsky, Jason  and  Leonard, Brian  and  {Van Durme}, Benjamin},
  title     = {Gender Bias in Coreference Resolution},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics}
}

Contact

If you have questions about this work or data, please contact Rachel Rudinger (rudinger AT jhu DOT edu).

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