Combining Word Reordering Methods on different Linguistic Abstraction Levels for Statistical Machine Translation
Teresa Herrmann, Jan Niehues, Alex Waibel · 2013
We describe a novel approach to combin-ing lexicalized, POS-based and syntactic tree-based word reordering in a phrase-based ma-chine translation system. Our results show that each of the presented reordering meth-ods 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 German-French 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 reorder-ing over a baseline with lexicalized reorder-ing. A human analysis, comparing subjec-tive translation quality as well as a detailed er-ror analysis show the impact of our presented tree-based rules in terms of improved sentence quality and reduction of errors related to miss-ing verbs and verb positions. 1