The FBK Participation in the WMT 2016 Automatic Post-editing Shared Task
Rajen Chatterjee, José G. C. de Souza, Matteo Negri, Marco Turchi · 2016
In this paper, we present a novel approach to combine the two variants of phrasebased APE (monolingual and contextaware) 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.