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

Understand

This paper describes the submission of the AMU (Adam Mickiewicz University) team to the Automatic Post-Editing (APE) task of WMT 2016.

• We explore the application of neural translation models to the APE problem and achieve good results by treating different models as components in a log-linear model, allowing for multiple inputs (the MT-output and the source) that are decoded to the same target language (post-edited translations).
• A simple string-matching penalty integrated within the log-linear model is used to control for higher faithfulness with regard to the raw machine translation output.
• To overcome the problem of too little training data, we generate large amounts of artificial data.