Neural Post-Editing Based on Quality Estimation

Automatic post-editing (APE) is a challenging task on WMT evaluation campaign. We find that only a small number of edit operations are required for most machine translation outputs, through analysis of the training set of WMT17 APE en-de task. Based on this statistics analysis, two neural postediting (NPE) models are trained depended on the edit numbers: single edit and minor edits. The improved quality estimation (QE) approach is exploited to rank models, and select the best translation as the post-edited output from the n-best list translation hypotheses generated by the best APE model and the raw translation system. Experimental results on the datasets of WMT16 APE test set show that the proposed approach significantly outperformed the baseline. Our approach can bring considerable relief from the overcorrection problem in APE.