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This paper describes the submission of the AMU (Adam Mickiewicz University)team to the Automatic Post-Editing (APE) task of WMT 2016. We explore theapplication of neural translation models to the APE problem and achieve goodresults by treating different models as components in a log-linear model,allowing for multiple inputs (the MT-output and the source) that are decoded tothe same target language (post-edited translations). A simple string-matchingpenalty integrated within the log-linear model is used to control for higherfaithfulness with regard to the raw machine translation output. To overcome theproblem of too little training data, we generate large amounts of artificialdata. Our submission improves over the uncorrected baseline on the unseen testset by -3.2\% TER and +5.5\% BLEU and outperforms any other system submitted tothe shared-task by a large margin.

Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing