  Word embeddings derived from human-generated corpora inherit strong gender
bias which can be further amplified by downstream models. Some commonly adopted
debiasing approaches, including the seminal Hard Debias algorithm, apply
post-processing procedures that project pre-trained word embeddings into a
subspace orthogonal to an inferred gender subspace. We discover that
semantic-agnostic corpus regularities such as word frequency captured by the
word embeddings negatively impact the performance of these algorithms. We
propose a simple but effective technique, Double Hard Debias, which purifies
the word embeddings against such corpus regularities prior to inferring and
removing the gender subspace. Experiments on three bias mitigation benchmarks
show that our approach preserves the distributional semantics of the
pre-trained word embeddings while reducing gender bias to a significantly
larger degree than prior approaches.
