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

• URL: http://arxiv.org/abs/2012.15547v1
• Date: Thu, 31 Dec 2020 11:16:51 GMT
• Title: XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders
• Authors: 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
• Abstract summary: We present XLM-T, which initializes the model with an off-the-shelf pretrained cross-lingual Transformer 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.
• Score: 89.0059978016914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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