  This paper explores augmenting monolingual data for knowledge distillation in
neural machine translation. Source language monolingual text can be
incorporated as a forward translation. Interestingly, we find the best way to
incorporate target language monolingual text is to translate it to the source
language and round-trip translate it back to the target language, resulting in
a fully synthetic corpus. We find that combining monolingual data from both
source and target languages yields better performance than a corpus twice as
large only in one language. Moreover, experiments reveal that the improvement
depends upon the provenance of the test set. If the test set was originally in
the source language (with the target side written by translators), then forward
translating source monolingual data matters. If the test set was originally in
the target language (with the source written by translators), then
incorporating target monolingual data matters.
