Modeling with Structures in Statistical Machine Translation
Conference Paper, Proceedings of 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics (ACL '98/COLING '98), Vol. 2, pp. 1357 - 1363, August, 1998
Abstract
Most statistical machine translation systems employ a word-based alignment model. In this paper we demonstrate that word-based alignment is a major cause of translation errors. We propose a new alignment model based on shallow phrase structures, and the structures can be automatically acquired from parallel corpus. This new model achieved over 10% error reduction for our spoken language translation task.
BibTeX
@conference{Wang-1998-16602,author = {Ye-Yi Wang and Alex Waibel},
title = {Modeling with Structures in Statistical Machine Translation},
booktitle = {Proceedings of 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics (ACL '98/COLING '98)},
year = {1998},
month = {August},
volume = {2},
pages = {1357 - 1363},
}
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