Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach

Automated Post-Editing (PE) is the task of automatically correcting common and repetitive errors found in machine translation (MT) output. In this paper, we present a neural programmer-interpreter approach to this task, resembling the way that humans perform postediting using discrete edit operations, which we refer to as programs. Our model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores.