A Finite-State Approach to Machine Translation
نویسندگان
چکیده
The problem of machine translation can be viewed as consisting of two subproblems (a) Lexical Selection and (b) Lexical Reordering. We propose stochas-tic nite-state models for these two subproblems in this paper. Stochastic nite-state models are ee-ciently learnable from data, eeective for decoding and are associated with a calculus for composing models which allows for tight integration of constraints from various levels of language processing. We present a method for learning stochastic nite-state models for lexical choice and lexical reordering that are trained automatically from pairs of source and target utterances. We use this method to develop models for English-Japanese translation and present the performance of these models for translation on speech and text. We also evaluate the ef-cacy of such a translation model in the context of a call routing task of unconstrained speech utterances .
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