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This work aims to produce translations that convey source language content ata formality level that is appropriate for a particular audience. Framing thisproblem as a neural sequence-to-sequence task ideally requires trainingtriplets consisting of a bilingual sentence pair labeled with target languageformality. However, in practice, available training examples are limited toEnglish sentence pairs of different styles, and bilingual parallel sentences ofunknown formality. We introduce a novel training scheme for multi-task modelsthat automatically generates synthetic training triplets by inferring themissing element on the fly, thus enabling end-to-end training. Comprehensiveautomatic and human assessments show that our best model outperforms existingmodels by producing translations that better match desired formality levelswhile preserving the source meaning.

Controlling Neural Machine Translation Formality with Synthetic Supervision