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Multilingual machine translation enables a single model to translate betweendifferent languages. Most existing multilingual machine translation systemsadopt a randomly initialized Transformer backbone. In this work, inspired bythe recent success of language model pre-training, we present XLM-T, whichinitializes the model with an off-the-shelf pretrained cross-lingualTransformer encoder and fine-tunes it with multilingual parallel data. Thissimple method achieves significant improvements on a WMT dataset with 10language pairs and the OPUS-100 corpus with 94 pairs. Surprisingly, the methodis also effective even upon the strong baseline with back-translation.Moreover, extensive analysis of XLM-T on unsupervised syntactic parsing, wordalignment, and multilingual classification explains its effectiveness formachine translation. The code will be at https://aka.ms/xlm-t.

XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders