  Multilingual neural machine translation (NMT) enables training a single model
that supports translation from multiple source languages into multiple target
languages. In this paper, we push the limits of multilingual NMT in terms of
number of languages being used. We perform extensive experiments in training
massively multilingual NMT models, translating up to 102 languages to and from
English within a single model. We explore different setups for training such
models and analyze the trade-offs between translation quality and various
modeling decisions. We report results on the publicly available TED talks
multilingual corpus where we show that massively multilingual many-to-many
models are effective in low resource settings, outperforming the previous
state-of-the-art while supporting up to 59 languages. Our experiments on a
large-scale dataset with 102 languages to and from English and up to one
million examples per direction also show promising results, surpassing strong
bilingual baselines and encouraging future work on massively multilingual NMT.
