Multilingual Unsupervised Neural Machine Translation with Denoising Adapters
Ahmet Üstün, Alexandre Berard, Laurent Besacier, Matthias Gallé
Abstract
We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data by using auxiliary parallel language pairs. For this problem the standard procedure so far to leverage the monolingual data is _back-translation_, which is computationally costly and hard to tune. In this paper we propose instead to use _denoising adapters_, adapter layers with a denoising objective, on top of pre-trained mBART-50. In addition to the modularity and flexibility of such an approach we show that the resulting translations are on-par with back-translating as measured by BLEU, and furthermore it allows adding unseen languages incrementally.
BibTeX
@inproceedings{ustun-etal-2021-multilingual,
title = "Multilingual Unsupervised Neural Machine Translation with Denoising Adapters",
author = {{\"U}st{\"u}n, Ahmet and
Berard, Alexandre and
Besacier, Laurent and
Gall{\'e}, Matthias},
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.533/",
doi = "10.18653/v1/2021.emnlp-main.533",
pages = "6650--6662"
}