  Multilingual machine translation, which translates multiple languages with a
single model, has attracted much attention due to its efficiency of offline
training and online serving. However, traditional multilingual translation
usually yields inferior accuracy compared with the counterpart using individual
models for each language pair, due to language diversity and model capacity
limitations. In this paper, we propose a distillation-based approach to boost
the accuracy of multilingual machine translation. Specifically, individual
models are first trained and regarded as teachers, and then the multilingual
model is trained to fit the training data and match the outputs of individual
models simultaneously through knowledge distillation. Experiments on IWSLT, WMT
and Ted talk translation datasets demonstrate the effectiveness of our method.
Particularly, we show that one model is enough to handle multiple languages (up
to 44 languages in our experiment), with comparable or even better accuracy
than individual models.
