First the worst: Finding better gender translations during beam search

• URL: http://arxiv.org/abs/2104.07429v1
• Date: Thu, 15 Apr 2021 12:53:30 GMT
• Title: First the worst: Finding better gender translations during beam search
• Authors: Danielle Saunders and Rosie Sallis and Bill Byrne
• Abstract summary: We focus on gender bias resulting from systematic errors in grammatical gender translation. We experiment with reranking nbest lists using gender features obtained automatically from the source sentence. We find that a combination of these techniques allows large gains in WinoMT accuracy without requiring additional bilingual data or an additional NMT model.
• Score: 19.921216907778447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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