  The resolution of ambiguous pronouns is a longstanding challenge in Natural
Language Understanding. Recent studies have suggested gender bias among
state-of-the-art coreference resolution systems. As an example, Google AI
Language team recently released a gender-balanced dataset and showed that
performance of these coreference resolvers is significantly limited on the
dataset. In this paper, we propose an extractive question answering (QA)
formulation of pronoun resolution task that overcomes this limitation and shows
much lower gender bias (0.99) on their dataset. This system uses fine-tuned
representations from the pre-trained BERT model and outperforms the existing
baseline by a significant margin (22.2% absolute improvement in F1 score)
without using any hand-engineered features. This QA framework is equally
performant even without the knowledge of the candidate antecedents of the
pronoun. An ensemble of QA and BERT-based multiple choice and sequence
classification models further improves the F1 (23.3% absolute improvement upon
the baseline). This ensemble model was submitted to the shared task for the 1st
ACL workshop on Gender Bias for Natural Language Processing. It ranked 9th on
the final official leaderboard. Source code is available at
https://github.com/rakeshchada/corefqa
