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A Neural Network based Approach to Automatic Post-Editing
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
doi:10.18653/v1/p16-2046
dblp:conf/acl/PalNVG16
fatcat:gtfxfrrcyjhjlp7mormlfaxusm