We invite work on gender-fair modeling via our shared task, coreference resolution on GAP (Webster et al. 2018). GAP is a coreference dataset designed to highlight current challenges for the resolution of ambiguous pronouns in context. GAP is a gender-balanced dataset and evaluation is gender disaggregated. Previous work has shown state-of-the-art resolvers are biased to yield better performance on masculine pronouns due to differences in the public discourse between genders. Participation will be via Kaggle, with submissions open over a three month period in the lead up to the workshop.
Competition is now open!
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- Jan 21 Public leaderboard opens for system development
- April 15-21 Test phase (official test data available)
- April 26 Results announced
- May 3 Submission of system description papers
- May 24 Description paper reviews completed
- June 7 Camera-ready papers due
- August 2 Workshop