  Neural machine translation (NMT) has recently achieved impressive results. A
potential problem of the existing NMT algorithm, however, is that the decoding
is conducted from left to right, without considering the right context. This
paper proposes an two-stage approach to solve the problem. In the first stage,
a conventional attention-based NMT system is used to produce a draft
translation, and in the second stage, a novel double-attention NMT system is
used to refine the translation, by looking at the original input as well as the
draft translation. This drafting-and-refinement can obtain the right-context
information from the draft, hence producing more consistent translations. We
evaluated this approach using two Chinese-English translation tasks, one with
44k pairs and 1M pairs respectively. The experiments showed that our approach
achieved positive improvements over the conventional NMT system: the
improvements are 2.4 and 0.9 BLEU points on the small-scale and large-scale
tasks, respectively.
