Neural Automatic Post-Editing Using Prior Alignment and Reranking

Abstract

We present a second-stage machine translation (MT) system based on a neural machine translation (NMT) approach to automatic post-editing (APE) that improves the translation quality provided by a firststage MT system. Our APE system (AP ESym) is an extended version of an attention based NMT model with bilingual symmetry employing bidirectional models, mt → pe and pe → mt. APE translations produced by our system show statistically significant improvements over the first-stage MT, phrase-based APE and the best reported score on the WMT 2016 APE dataset by a previous neural APE system. Re-ranking (AP ERerank) of the n-best translations from the phrase-based APE and AP ESym systems provides further substantial improvements over the symmetric neural APE model. Human evaluation confirms that the AP ERerank generated PE translations improve on the previous best neural APE system at WMT 2016.Santanu Pal is supported by the People Programme (Marie Curie Actions) of the European Union’s Framework Programme (FP7/2007-2013) under REA grant agreement no 317471. Sudip Kumar Naskar is supported by Media Lab Asia, MeitY, Government of India, under the Young Faculty Research Fellowship of the Visvesvaraya PhD Scheme for Electronics & IT. Qun Liu and Josef van Genabith is supported by funding from the European Union Horizon 2020 research and innovation programme under grant agreement no 645452 (QT21)