  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.
