Shared Task

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!

Please visit this link for more details

Join our mailing-list <genderbiasnlp-sharedtask-2019@googlegroups.com>

See this page for important dates.

We have also prepared some notes on how to write a shared task system description paper.

Results

Congratulations to all participants, and especially the top 3 systems who won prizes generously provided by by Google.