  For Japanese-to-English translation, zero pronouns in Japanese pose a
challenge, since the model needs to infer and produce the corresponding pronoun
in the target side of the English sentence. However, although fully resolving
zero pronouns often needs discourse context, in some cases, the local context
within a sentence gives clues to the inference of the zero pronoun. In this
study, we propose a data augmentation method that provides additional training
signals for the translation model to learn correlations between local context
and zero pronouns. We show that the proposed method significantly improves the
accuracy of zero pronoun translation with machine translation experiments in
the conversational domain.
