A Neural Network based Approach to Automatic Post-Editing

Santanu Pal, Sudip Kumar Naskar, Mihaela Vela, Josef van Genabith
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
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
more » ... , 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.
doi:10.18653/v1/p16-2046 dblp:conf/acl/PalNVG16 fatcat:gtfxfrrcyjhjlp7mormlfaxusm