  The translation of gender-neutral person-referring terms (e.g., the students)
is often non-trivial. Translating from English into German poses an interesting
case -- in German, person-referring nouns are usually gender-specific, and if
the gender of the referent(s) is unknown or diverse, the generic masculine (die
Studenten (m.)) is commonly used. This solution, however, reduces the
visibility of other genders, such as women and non-binary people. To counteract
gender discrimination, a societal movement towards using gender-fair language
exists (e.g., by adopting neosystems). However, gender-fair German is currently
barely supported in machine translation (MT), requiring post-editing or manual
translations. We address this research gap by studying gender-fair language in
English-to-German MT. Concretely, we enrich a community-created gender-fair
language dictionary and sample multi-sentence test instances from encyclopedic
text and parliamentary speeches. Using these novel resources, we conduct the
first benchmark study involving two commercial systems and six neural MT models
for translating words in isolation and natural contexts across two domains. Our
findings show that most systems produce mainly masculine forms and rarely
gender-neutral variants, highlighting the need for future research. We release
code and data at
https://github.com/g8a9/building-bridges-gender-fair-german-mt.
