  We introduce the task of zero-shot style transfer between different
languages. Our training data includes multilingual parallel corpora, but does
not contain any parallel sentences between styles, similarly to the recent
previous work. We propose a unified multilingual multi-style machine
translation system design, that allows to perform zero-shot style conversions
during inference; moreover, it does so both monolingually and cross-lingually.
Our model allows to increase the presence of dissimilar styles in corpus by up
to 3 times, easily learns to operate with various contractions, and provides
reasonable lexicon swaps as we see from manual evaluation.

Computation and Language