![PronounResolution](https://storage.googleapis.com/kaggle-media/competitions/GoogleAI-GenderedPronoun/PronounResolution.png)
Can you help end gender bias in pronoun resolution?
Pronoun resolution is part of coreference resolution, the task of pairing an expression to its referring entity. This is an important task for natural language understanding, and the resolution of ambiguous pronouns is a longstanding challenge.
Unfortunately, recent studies have suggested gender bias among state-of-the-art coreference resolvers.
[Google AI Language](https://ai.google/research/teams/language/)
aims to improve gender-fairness in modeling by releasing the
[Gendered Ambiguous Pronouns (GAP) dataset](http://goo.gl/language/gap-coreference)
, containing gender-balanced pronouns (50% of its examples containing feminine pronouns, and 50% containing masculine pronouns).
In this
[two-stage competition](https://www.kaggle.com/two-stage-frequently-asked-questions)
, Kagglers are challenged to build pronoun resolution systems that perform equally well regardless of pronoun gender. Stage two's final evaluation will use a new dataset following the same format. To encourage gender-fair modeling, the ratio of masculine to feminine examples in the official test data will not be known ahead of time.
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Please cite the original paper if you use GAP in your work:
@inproceedings{webster2018gap,
  title =     {Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns},
  author =    {Webster, Kellie and Recasens, Marta and Axelrod, Vera and Baldridge, Jason},
  booktitle = {Transactions of the ACL},
  year =      {2018},
  pages =     {to appear},
}