A preprint paper published by University of Trento researchers proposes a benchmark — MuST-SHE — to evaluate whether speech translation systems fed textual data are constrained by the fact that sentences sometimes omit gender identity clues. The coauthors assert that these systems can and do exhibit gender bias, and that signals beyond text (like audio) provide contextual clues that might reduce this bias.

Continue reading.