What about em? How Commercial Machine Translation Fails to Handle
  (Neo-)Pronouns
Anne Lauscher Debora Nozza Archie Crowley Ehm Miltersen Dirk Hovy
As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns,
we need more research on identity-inclusive NLP. Exclusion is particularly
harmful in one of the most popular NLP applications, machine translation (MT).
Wrong pronoun translations can discriminate against marginalized groups, e.g.,
non-binary individuals (Dev et al., 2021). In this ``reality check'', we study
how three commercial MT systems translate 3rd-person pronouns. Concretely, we
compare the translations of gendered vs. gender-neutral pronouns from English
to five other languages (Danish, Farsi, French, German, Italian), and vice
versa, from Danish to English. Our error analysis shows that the presence of a
gender-neutral pronoun often leads to grammatical and semantic translation
errors. Similarly, gender neutrality is often not preserved. By surveying the
opinions of affected native speakers from diverse languages, we provide
recommendations to address the issue in future MT research.