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.
@article{arxiv.1808.00179,
title = {Monolingual and Cross-lingual Zero-shot Style Transfer},
author = {Elizaveta Korotkova and Maksym Del and Mark Fishel},
journal= {arXiv preprint arXiv:1808.00179},
year = {2018}
}