- T. Herrmann, J. Niehues, A. Waibel. 2013. Combining Word Reordering Methods on different Linguistic Abstraction Levels for Statistical Machine Translation. Abstract: We describe a novel approach to combining lexicalized, POS-based and syntactic treebased word reordering in a phrase-based machine translation system. Our results show that each of the presented reordering methods leads to improved translation quality on its own. The strengths however can be combined to achieve further improvements. We present experiments on German-English and GermanFrench translation. We report improvements of 0.7 BLEU points by adding tree-based and lexicalized reordering. Up to 1.1 BLEU points can be gained by POS and tree-based reordering over a baseline with lexicalized reordering. A human analysis, comparing subjective translation quality as well as a detailed error analysis show the impact of our presented tree-based rules in terms of improved sentence quality and reduction of errors related to missing verbs and verb positions.
