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Neural machine translation inference procedures like beam search generate themost likely output under the model. This can exacerbate any demographic biasesexhibited by the model. We focus on gender bias resulting from systematicerrors in grammatical gender translation, which can lead to human referentsbeing misrepresented or misgendered.  Most approaches to this problem adjust the training data or the model. Bycontrast, we experiment with simply adjusting the inference procedure. Weexperiment with reranking nbest lists using gender features obtainedautomatically from the source sentence, and applying gender constraints whiledecoding to improve nbest list gender diversity. We find that a combination ofthese techniques allows large gains in WinoMT accuracy without requiringadditional bilingual data or an additional NMT model.

First the worst: Finding better gender translations during beam search