Neural Machine Translation (NMT) has been shown to struggle with grammatical
gender that is dependent on the gender of human referents, which can cause
gender bias effects. Many existing approaches to this problem seek to control
gender inflection in the target language by explicitly or implicitly adding a
gender feature to the source sentence, usually at the sentence level.
In this paper we propose schemes for incorporating explicit word-level gender
inflection tags into NMT. We explore the potential of this gender-inflection
controlled translation when the gender feature can be determined from a human
reference, or when a test sentence can be automatically gender-tagged,
assessing on English-to-Spanish and English-to-German translation.
We find that simple existing approaches can over-generalize a gender-feature
to multiple entities in a sentence, and suggest effective alternatives in the
form of tagged coreference adaptation data. We also propose an extension to
assess translations of gender-neutral entities from English given a
corresponding linguistic convention, such as a non-binary inflection, in the
target language.

本文提出了一种将显式词级别的性别标记融入 NMT 中的方案，并探讨了在确定性别特征来源以及在目标语言中实现类似非二元词汇变化的情况下的性别标记控制翻译。该文发现了一些现有方法可能出现性别特征推广至句子中的多个实体，并提出了有效的替代方案，包括标记共指适应数据。