Paper: Learning Lexicalized Reordering Models from Reordering Graphs

ACL ID P10-2003
Title Learning Lexicalized Reordering Models from Reordering Graphs
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

Lexicalized reordering models play a crucial role in phrase-based translation systems. They are usually learned from the word-aligned bilingual corpus by examining the reordering relations of adjacent phrases. Instead of just checking whether there is one phrase adjacent to a given phrase, we argue that it is important to take the number of adjacent phrases into account for better estimations of reordering models. We propose to use a structure named reordering graph, which represents all phrase segmentations of a sentence pair, to learn lex- icalized reordering models efficiently. Exper- imental results on the NIST Chinese-English test sets show that our approach significantly outperforms the baseline method.