  Discourse coherence plays an important role in the translation of one text.
However, the previous reported models most focus on improving performance over
individual sentence while ignoring cross-sentence links and dependencies, which
affects the coherence of the text. In this paper, we propose to use discourse
context and reward to refine the translation quality from the discourse
perspective. In particular, we generate the translation of individual sentences
at first. Next, we deliberate the preliminary produced translations, and train
the model to learn the policy that produces discourse coherent text by a reward
teacher. Practical results on multiple discourse test datasets indicate that
our model significantly improves the translation quality over the
state-of-the-art baseline system by +1.23 BLEU score. Moreover, our model
generates more discourse coherent text and obtains +2.2 BLEU improvements when
evaluated by discourse metrics.
