The FBK Participation in the WMT 2016 Automatic Post-editing Shared Task

In this paper, we present a novel approach to combine the two variants of phrase-based APE (monolingual and context-aware) by a factored machine translation model that is able to leverage benefits from both. Our factored APE models include part-of-speech-tag and class-based neural language models (LM) along with statistical word-based LM to improve the fluency of the post-edits. These models are built upon a data augmentation technique which helps to mitigate the problem of over-correction in phrase-based APE systems. Our primary APE system further incorporates a quality estimation (QE) model, which aims to select the best translation between the MT output and the automatic post-edit. According to the shared task results, our primary and contrastive (which does not include the QE module) submissions have similar performance and achieved significant improvement of 3.31% TER and 4.25% BLEU (relative) over the baseline MT system on the English-German evaluation set.