  In this paper, we quantify, analyze and mitigate gender bias exhibited in
ELMo's contextualized word vectors. First, we conduct several intrinsic
analyses and find that (1) training data for ELMo contains significantly more
male than female entities, (2) the trained ELMo embeddings systematically
encode gender information and (3) ELMo unequally encodes gender information
about male and female entities. Then, we show that a state-of-the-art
coreference system that depends on ELMo inherits its bias and demonstrates
significant bias on the WinoBias probing corpus. Finally, we explore two
methods to mitigate such gender bias and show that the bias demonstrated on
WinoBias can be eliminated.
