Detailed Analysis of different Strategies for Phrase Table Adaptation in SMT

This paper gives a detailed analysis of different approaches to adapt a statistical machine translation system towards a target domain using small amounts of parallel in-domain data. Therefore, we investigate the differences between the approaches addressing adaptation on the two main steps of building a translation model: The candidate selection and the phrase scoring. For the latter step we characterized the differences by four key aspects. We performed experiments on two different tasks of speech translation and analyzed the influence of the different aspects on the overall translation quality. On both tasks we could show significant improvements by using the presented adaptation techniques.