A neural network based approach to automatic post-editing

Abstract

We present a neural network based automatic post-editing (APE) system to improve raw machine translation (MT) output. Our neural model of APE (NNAPE) is based on a bidirectional recurrent neural network (RNN) model and consists of an encoder that encodes an MT output into a fixed-length vector from which a decoder provides a post-edited (PE) translation. APE translations produced by NNAPE show statistically significant improvements of 3.96, 2.68 and 1.35 BLEU points absolute over the original MT, phrase-based APE and hierarchical APE outputs, respectively. Furthermore, human evaluation shows that the NNAPE generated PE translations are much better than the original MT output.