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The resolution of ambiguous pronouns is a longstanding challenge in NaturalLanguage Understanding. Recent studies have suggested gender bias amongstate-of-the-art coreference resolution systems. As an example, Google AILanguage team recently released a gender-balanced dataset and showed thatperformance of these coreference resolvers is significantly limited on thedataset. In this paper, we propose an extractive question answering (QA)formulation of pronoun resolution task that overcomes this limitation and showsmuch lower gender bias (0.99) on their dataset. This system uses fine-tunedrepresentations from the pre-trained BERT model and outperforms the existingbaseline by a significant margin (22.2% absolute improvement in F1 score)without using any hand-engineered features. This QA framework is equallyperformant even without the knowledge of the candidate antecedents of thepronoun. An ensemble of QA and BERT-based multiple choice and sequenceclassification models further improves the F1 (23.3% absolute improvement uponthe baseline). This ensemble model was submitted to the shared task for the 1stACL workshop on Gender Bias for Natural Language Processing. It ranked 9th onthe final official leaderboard. Source code is available athttps://github.com/rakeshchada/corefqa

Gendered Pronoun Resolution using BERT and an extractive question answering formulation