  Neural machine translation on low-resource language is challenging due to the
lack of bilingual sentence pairs. Previous works usually solve the low-resource
translation problem with knowledge transfer in a multilingual setting. In this
paper, we propose the concept of Language Graph and further design a novel
graph distillation algorithm that boosts the accuracy of low-resource
translations in the graph with forward and backward knowledge distillation.
Preliminary experiments on the TED talks multilingual dataset demonstrate the
effectiveness of our proposed method. Specifically, we improve the low-resource
translation pair by more than 3.13 points in terms of BLEU score.
