  This work aims to produce translations that convey source language content at
a formality level that is appropriate for a particular audience. Framing this
problem as a neural sequence-to-sequence task ideally requires training
triplets consisting of a bilingual sentence pair labeled with target language
formality. However, in practice, available training examples are limited to
English sentence pairs of different styles, and bilingual parallel sentences of
unknown formality. We introduce a novel training scheme for multi-task models
that automatically generates synthetic training triplets by inferring the
missing element on the fly, thus enabling end-to-end training. Comprehensive
automatic and human assessments show that our best model outperforms existing
models by producing translations that better match desired formality levels
while preserving the source meaning.
