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With the advent of neural machine translation, there has been a marked shifttowards leveraging and consuming the machine translation results. However, thegap between machine translation systems and human translators needs to bemanually closed by post-editing. In this paper, we propose an end-to-end deeplearning framework of the quality estimation and automatic post-editing of themachine translation output. Our goal is to provide error correction suggestionsand to further relieve the burden of human translators through an interpretablemodel. To imitate the behavior of human translators, we design three efficientdelegation modules -- quality estimation, generative post-editing, and atomicoperation post-editing and construct a hierarchical model based on them. Weexamine this approach with the English--German dataset from WMT 2017 APE sharedtask and our experimental results can achieve the state-of-the-art performance.We also verify that the certified translators can significantly expedite theirpost-editing processing with our model in human evaluation.

Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing