Multilingual Denoising Pre training for Neural Machine Translation
Abstract This paper demonstrates that multilingual denoising pre training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART a sequence to sequence denoising auto encoder pre trained on large scale monolingual corpora in many languages using the BART objective (Lewis et al. 2019) mBART is the first method for pre training a complete sequence to sequence model by denoising full texts in multiple languages, whereas previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text. Pre training a complete model allows it to be directly fine tuned for supervised (both sentence level and document level) and unsupervised machine translation, with no task specific modifications. We demonstrate that adding mBART initialization produces performance gains in all but the highest resource settings, including up to 12 BLEU points for low resource MT and over 5 BLEU points for many document level and unsupervised models. We also show that it enables transfer to language pairs with no bi text or that were not in the pre training corpus, and present extensive analysis of which factors contribute the most to effective pre training.1
Transactions of the Association for Computational Linguistics