An efficient phrase-to-phrase alignment model for arbitrarily long phrase and large corpora

An efficient phrase-to-phrase alignment model for arbitrarily long phrase and large corpora (2005)

by Ying Zhang , Stephan Vogel

Venue:	In Proceedings of the 10th Conference of the European Association for Machine Translation (EAMT-05
Citations:	10 - 5 self

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Datum	Value	Source
TITLE	An efficient phrase-to-phrase alignment model for arbitrarily long phrase and large corpora	INFERENCE
AUTHOR NAME	Ying Zhang	user correction
AUTHOR AFFIL	Language Technologies Institute; School of Computer Science; Carnegie Mellon University	user correction
AUTHOR NAME	Stephan Vogel	user correction
AUTHOR AFFIL	Language Technologies Institute; School of Computer Science; Carnegie Mellon University	user correction
ABSTRACT	Most statistical machine translation (SMT) systems use phrase-to-phrase translations to capture local context information, leading to better lexical choices and more reliable word reordering. Long phrases capture more contexts than short phrases and result in better translation qualities. On the other hand, the increasing amount of bilingual data poses serious problems for storing all possible phrases. In this paper, we describe a novel phrase-to-phrase alignment model which allows for arbitrarily long phrases and works for very large bilingual corpora. This model is very efficient in both time and space and the resulting translations are better than the state-of-the-art systems.	user correction
YEAR	2005	INFERENCE
VENUE	In Proceedings of the 10th Conference of the European Association for Machine Translation (EAMT-05	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	30--31	INFERENCE
CITATIONS	14 found	ParsCit 1.0