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 E Sym ) 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 E Rerank ) of the n-best translations from the phrase-based APE and AP E Sym systems provides further substantial improvements over the symmetric neural APE model. Human evaluation confirms that the AP E Rerank generated PE translations improve on the previous best neural APE system at WMT 2016.
Assuming that post-editing is reversible, REF have proposed an attention mechanism over bidirectional models, mt→ pe and pe → mt.
Neural Automatic Post-Editing Using Prior Alignment and Reranking
EACL
Computer Science