  The vast majority of work on gender in MT focuses on 'unambiguous' inputs,
where gender markers in the source language are expected to be resolved in the
output. Conversely, this paper explores the widespread case where the source
sentence lacks explicit gender markers, but the target sentence contains them
due to richer grammatical gender. We particularly focus on inputs containing
person names.
  Investigating such sentence pairs casts a new light on research into MT
gender bias and its mitigation. We find that many name-gender co-occurrences in
MT data are not resolvable with 'unambiguous gender' in the source language,
and that gender-ambiguous examples can make up a large proportion of training
examples. From this, we discuss potential steps toward gender-inclusive
translation which accepts the ambiguity in both gender and translation.
