  Many text corpora exhibit socially problematic biases, which can be
propagated or amplified in the models trained on such data. For example, doctor
cooccurs more frequently with male pronouns than female pronouns. In this study
we (i) propose a metric to measure gender bias; (ii) measure bias in a text
corpus and the text generated from a recurrent neural network language model
trained on the text corpus; (iii) propose a regularization loss term for the
language model that minimizes the projection of encoder-trained embeddings onto
an embedding subspace that encodes gender; (iv) finally, evaluate efficacy of
our proposed method on reducing gender bias. We find this regularization method
to be effective in reducing gender bias up to an optimal weight assigned to the
loss term, beyond which the model becomes unstable as the perplexity increases.
We replicate this study on three training corpora---Penn Treebank, WikiText-2,
and CNN/Daily Mail---resulting in similar conclusions.
