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We present our 7th place solution to the Gendered Pronoun Resolutionchallenge, which uses BERT without fine-tuning and a novel augmentationstrategy designed for contextual embedding token-level tasks. Our methodanonymizes the referent by replacing candidate names with a set of commonplaceholder names. Besides the usual benefits of effectively increasingtraining data size, this approach diversifies idiosyncratic informationembedded in names. Using same set of common first names can also help the modelrecognize names better, shorten token length, and remove gender and regionalbiases associated with names. The system scored 0.1947 log loss in stage 2,where the augmentation contributed to an improvements of 0.04. Post-competitionanalysis shows that, when using different embedding layers, the system scores0.1799 which would be third place.

Anonymized BERT: An Augmentation Approach to the Gendered Pronoun Resolution Challenge