Recent work using word embeddings-low-dimensional vector representations of words trained on large datasets to capture key semantic informationhas demonstrated that language encodes several gender, racial, and other common contemporary biases that correlate with both implicit biases (Caliskan et al., 2017) and macro-scale historical trends <cite>(Garg et al., 2018)</cite> . For example, the historical biases presented in <cite>(Garg et al., 2018)</cite> are computed using decade-specific word embeddings produced by training different Word2Vec (Mikolov et al., 2013 ) models on a large corpus of historical text from that decade.