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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 rules 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, |
