Paper: A Clustered Global Phrase Reordering Model For Statistical Machine Translation

ACL ID P06-1090
Title A Clustered Global Phrase Reordering Model For Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

In this paper, we present a novel global re- ordering model that can be incorporated into standard phrase-based statistical ma- chine translation. Unlike previous local reordering models that emphasize the re- ordering of adjacent phrase pairs (Till- mann and Zhang, 2005), our model ex- plicitly models the reordering of long dis- tances by directly estimating the parame- ters from the phrase alignments of bilin- gual training sentences. In principle, the global phrase reordering model is condi- tioned on the source and target phrases that are currently being translated, and the previously translated source and tar- get phrases. To cope with sparseness, we use N-best phrase alignments and bilin- gual phrase clustering, and investigate a variety of combinations of conditioning factors. Throu...