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

Shuming Ma; Jian Yang; Haoyang Huang; Zewen Chi; Li Dong; Dongdong Zhang; Hany Hassan Awadalla; Alexandre Muzio; Akiko Eriguchi; Saksham Singhal; Xia Song; Arul Menezes; Furu Wei

arXiv · 2012.15547

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

Shuming Ma·Jian Yang·Haoyang Huang·Zewen Chi·Li Dong·Dongdong Zhang·Hany Hassan Awadalla·Alexandre Muzio·Akiko Eriguchi·Saksham Singhal·Xia Song·Arul Menezes·Furu Wei

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Abstract

Multilingual machine translation enables a single model to translate between different languages. Most existing multilingual machine translation systems adopt a randomly initialized Transformer backbone. In this work, inspired by the recent success of language model pre-training, we present XLM-T, which initializes the model with an off-the-shelf pretrained cross-lingual Transformer encoder and fine-tunes it with multilingual parallel data. This simple method achieves significant improvements on a WMT dataset with 10 language pairs and the OPUS-100 corpus with 94 pairs. Surprisingly, the method is also effective even upon the strong baseline with back-translation. Moreover, extensive analysis of XLM-T on unsupervised syntactic parsing, word alignment, and multilingual classification explains its effectiveness for machine translation. The code will be at https://aka.ms/xlm-t.

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Shuming Ma, Jian Yang, Haoyang Huang, Zewen Chi, Li Dong, Dongdong Zhang, Hany Hassan Awadalla, Alexandre Muzio, Akiko Eriguchi, Saksham Singhal, Xia Song, Arul Menezes, Furu Wei. 2020-12-31. XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders. https://arxiv.org/abs/2012.15547

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