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
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 this https URL.