We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using machine translated data as well as gold aligned English sentences yields state-of-the-art results in the three target languages we consider. Besides, in view of the general scarcity of parallel data, we propose a modular approach for multilingual formality transfer, which consists of two training strategies that target adaptation to both language and task. Our approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages.
@article{arxiv.2203.08552,
title = {Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer},
author = {Huiyuan Lai and Antonio Toral and Malvina Nissim},
journal= {arXiv preprint arXiv:2203.08552},
year = {2022}
}