  Although recent years have brought significant progress in improving
translation of unambiguously gendered sentences, translation of ambiguously
gendered input remains relatively unexplored. When source gender is ambiguous,
machine translation models typically default to stereotypical gender roles,
perpetuating harmful bias. Recent work has led to the development of "gender
rewriters" that generate alternative gender translations on such ambiguous
inputs, but such systems are plagued by poor linguistic coverage. To encourage
better performance on this task we present and release GATE, a linguistically
diverse corpus of gender-ambiguous source sentences along with multiple
alternative target language translations. We also provide tools for evaluation
and system analysis when using GATE and use them to evaluate our translation
rewriter system.

Computation and Language