  With the advent of neural machine translation, there has been a marked shift
towards leveraging and consuming the machine translation results. However, the
gap between machine translation systems and human translators needs to be
manually closed by post-editing. In this paper, we propose an end-to-end deep
learning framework of the quality estimation and automatic post-editing of the
machine translation output. Our goal is to provide error correction suggestions
and to further relieve the burden of human translators through an interpretable
model. To imitate the behavior of human translators, we design three efficient
delegation modules -- quality estimation, generative post-editing, and atomic
operation post-editing and construct a hierarchical model based on them. We
examine this approach with the English--German dataset from WMT 2017 APE shared
task and our experimental results can achieve the state-of-the-art performance.
We also verify that the certified translators can significantly expedite their
post-editing processing with our model in human evaluation.
