  Neural machine translation inference procedures like beam search generate the
most likely output under the model. This can exacerbate any demographic biases
exhibited by the model. We focus on gender bias resulting from systematic
errors in grammatical gender translation, which can lead to human referents
being misrepresented or misgendered.
  Most approaches to this problem adjust the training data or the model. By
contrast, we experiment with simply adjusting the inference procedure. We
experiment with reranking nbest lists using gender features obtained
automatically from the source sentence, and applying gender constraints while
decoding to improve nbest list gender diversity. We find that a combination of
these techniques allows large gains in WinoMT accuracy without requiring
additional bilingual data or an additional NMT model.
