  Targeted evaluations have found that machine translation systems often output
incorrect gender, even when the gender is clear from context. Furthermore,
these incorrectly gendered translations have the potential to reflect or
amplify social biases. We propose a gender-filtered self-training technique to
improve gender translation accuracy on unambiguously gendered inputs. This
approach uses a source monolingual corpus and an initial model to generate
gender-specific pseudo-parallel corpora which are then added to the training
data. We filter the gender-specific corpora on the source and target sides to
ensure that sentence pairs contain and correctly translate the specified
gender. We evaluate our approach on translation from English into five
languages, finding that our models improve gender translation accuracy without
any cost to generic translation quality. In addition, we show the viability of
our approach on several settings, including re-training from scratch,
fine-tuning, controlling the balance of the training data, forward translation,
and back-translation.
