  Recent research has demonstrated that vector space models of semantics can
reflect undesirable biases in human culture. Our investigation of
crosslinguistic word embeddings reveals that topical gender bias interacts
with, and is surpassed in magnitude by, the effect of grammatical gender
associations, and both may be attenuated by corpus lemmatization. This finding
has implications for downstream applications such as machine translation.
