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.
|Title of host publication||Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics|
|Publisher||Association for Computational Linguistics, ACL Anthology|
|Publication status||E-pub ahead of print - 2022|
|Event||The 60th Annual Meeting of the Association for Computational Linguistics - Dublin, Ireland|
Duration: 22-May-2022 → 27-May-2022
|Conference||The 60th Annual Meeting of the Association for Computational Linguistics|
|Period||22/05/2022 → 27/05/2022|