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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/5664
Title: Machine translation with a stochastic grammatical channel
Authors: Wong, Hongsing
Issue Date: 1999
Abstract: 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 inversion-transduction 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. Experiments show that it also achieves significant speed gains over our earlier model.
Description: Thesis (M.Phil.)--Hong Kong University of Science and Technology, 1999
xii, 68 leaves : ill. ; 30 cm
HKUST Call Number: Thesis COMP 1999 Wong
URI: http://hdl.handle.net/1783.1/5664
Appears in Collections:CSE Master Theses

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