  Machine translation systems with inadequate document understanding can make
errors when translating dropped or neutral pronouns into languages with
gendered pronouns (e.g., English). Predicting the underlying gender of these
pronouns is difficult since it is not marked textually and must instead be
inferred from coreferent mentions in the context. We propose a novel
cross-lingual pivoting technique for automatically producing high-quality
gender labels, and show that this data can be used to fine-tune a BERT
classifier with 92% F1 for Spanish dropped feminine pronouns, compared with
30-51% for neural machine translation models and 54-71% for a non-fine-tuned
BERT model. We augment a neural machine translation model with labels from our
classifier to improve pronoun translation, while still having parallelizable
translation models that translate a sentence at a time.
