  Achieving universal translation between all human language pairs is the
holy-grail of machine translation (MT) research. While recent progress in
massively multilingual MT is one step closer to reaching this goal, it is
becoming evident that extending a multilingual MT system simply by training on
more parallel data is unscalable, since the availability of labeled data for
low-resource and non-English-centric language pairs is forbiddingly limited. To
this end, we present a pragmatic approach towards building a multilingual MT
model that covers hundreds of languages, using a mixture of supervised and
self-supervised objectives, depending on the data availability for different
language pairs. We demonstrate that the synergy between these two training
paradigms enables the model to produce high-quality translations in the
zero-resource setting, even surpassing supervised translation quality for low-
and mid-resource languages. We conduct a wide array of experiments to
understand the effect of the degree of multilingual supervision, domain
mismatches and amounts of parallel and monolingual data on the quality of our
self-supervised multilingual models. To demonstrate the scalability of the
approach, we train models with over 200 languages and demonstrate high
performance on zero-resource translation on several previously under-studied
languages. We hope our findings will serve as a stepping stone towards enabling
translation for the next thousand languages.
