Machine Translation with a Stochastic Grammatical Channel

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Machine Translation with a Stochastic Grammatical Channel
We introduce a stochastic grammatical channel model for machine translation, that synthesizes several 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 grammar 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 inversiontransduction model. However, unlike pure statistical translation models, the generated output string is guaranteed to conform to a given target grammar. 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 rules are used makes the model easily portable to a variety of source languages. Initial experiments show that it also achi...
Dekai Wu, Hongsing Wong
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1998
Where ACL
Authors Dekai Wu, Hongsing Wong
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