XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders. - Ma, Shuming; Yang, Jian; Huang, Haoyang; Chi, Zewen; Dong, Li; Zhang, Dongdong; Hassan Awadalla, Hany; Muzio, Alexandre; Eriguchi, Akiko I.; Singhal, Saksham; Song, Xia; Menezes, Arul; et al. (arXiv, 2020)
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.