Statistical post-editing (SPE) techniques have been successfully applied to the output of Rule Based MT (RBMT) systems. In this paper we investigate the impact of SPE on a standard Phrase-Based Statistical Machine Translation (PB-SMT) system, using PB-SMT both for the first-stage MT and the second stage SPE system. Our results show that, while a naive approach to using SPE in a PB-SMT pipeline produces no or only modest improvements, a novel combination of source context modelling and thresholding can produce statistically significant improvements of 2 BLEU points over baseline using technical translation data for French to English. 1

We improve translation memory (TM)inspired consistent phrase-based statistical machine translation (PB-SMT) using rich linguistic information including lexical, part-of-speech, dependency, and semantic role features to predict whether a TM-derived sub-segment should constrain PB-SMT translation. Besides better translation consistency, for English-to-Chinese Symantec TMs we report a 1.01 BLEU point improvement over a regular state-of-the-art PB-SMT system, and a 0.45 BLEU point improvement over a TM-constrained PB-SMT system without access to rich linguistic information, both statistically significant (p <0.01). We analyze the system output and summarize the benefits of using linguistic annotations to characterise the nature of translation consistency. 1