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 complete!
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We have also prepared some notes on how to write a shared task system description paper.
Congratulations to all participants, and especially the top 3 systems who won prizes generously provided by by Google.