  Recent studies have shown that word embeddings exhibit gender bias inherited
from the training corpora. However, most studies to date have focused on
quantifying and mitigating such bias only in English. These analyses cannot be
directly extended to languages that exhibit morphological agreement on gender,
such as Spanish and French. In this paper, we propose new metrics for
evaluating gender bias in word embeddings of these languages and further
demonstrate evidence of gender bias in bilingual embeddings which align these
languages with English. Finally, we extend an existing approach to mitigate
gender bias in word embeddings under both monolingual and bilingual settings.
Experiments on modified Word Embedding Association Test, word similarity, word
translation, and word pair translation tasks show that the proposed approaches
effectively reduce the gender bias while preserving the utility of the
embeddings.
