期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2010
卷号：2010
出版社：ACL Anthology
摘要：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 lexicalized
reordering models efficiently. Experimental
results on the NIST Chinese-English
test sets show that our approach significantly
outperforms the baseline method.
