  Recent work demonstrates the potential of multilingual pretraining of
creating one model that can be used for various tasks in different languages.
Previous work in multilingual pretraining has demonstrated that machine
translation systems can be created by finetuning on bitext. In this work, we
show that multilingual translation models can be created through multilingual
finetuning. Instead of finetuning on one direction, a pretrained model is
finetuned on many directions at the same time. Compared to multilingual models
trained from scratch, starting from pretrained models incorporates the benefits
of large quantities of unlabeled monolingual data, which is particularly
important for low resource languages where bitext is not available. We
demonstrate that pretrained models can be extended to incorporate additional
languages without loss of performance. We double the number of languages in
mBART to support multilingual machine translation models of 50 languages.
Finally, we create the ML50 benchmark, covering low, mid, and high resource
languages, to facilitate reproducible research by standardizing training and
evaluation data. On ML50, we demonstrate that multilingual finetuning improves
on average 1 BLEU over the strongest baselines (being either multilingual from
scratch or bilingual finetuning) while improving 9.3 BLEU on average over
bilingual baselines from scratch.
