Machine translation with a stochastic grammatical channel

Machine translation with a stochastic grammatical channel (1998)

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by Dekai Wu , Hongsing Wong

Venue:	In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL
Citations:	25 - 2 self

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Datum	Value	Source
TITLE	Machine translation with a stochastic grammatical channel	INFERENCE
AUTHOR NAME	Dekai Wu	SVM HeaderParse 0.2
AUTHOR AFFIL	HKUST	SVM HeaderParse 0.2
AUTHOR NAME	Hongsing Wong	SVM HeaderParse 0.2
AUTHOR AFFIL	HKUST	SVM HeaderParse 0.2
ABSTRACT	We introduce a stochastic grammatical channel model for machine translation, that synthesizes sev-eral desirable characteristics of both statistical and grammatical machine translation. As with the pure statistical translation model described by Wu (1996) (in which a bracketing transduction gram-mar models the channel), alternative hypotheses compete probabilistically, exhaustive search of the translation hypothesis space can be performed in polynomial time, and robustness heuristics arise naturally from a language-independent inversion-transduction model. However, unlike pure statisti-cal translation models, the generated output string is guaranteed to conform to a given target gram-mar. The model employs only (1) a translation lexicon, (2) a context-free grammar for the target language, and (3) a bigram language model. The fact that no explicit bilingual translation roles are used makes the model easily portable to a variety of source languages. Initial experiments show that it also achieves significant speed gains over our ear-lier model. 1	SVM HeaderParse 0.2
YEAR	1998	INFERENCE
VENUE	In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	1408--1414	INFERENCE
CITATIONS	5 found	ParsCit 1.0