Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddings

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