Title: A Finite-state Approach to Machine Translation

Abstract: The problem of machine translation can be viewed as consisting of two subproblems (a) Lexical Selection and (b) Lexical Reordering. We propose stochastic finite-state models for these two subproblems in this paper. Stochastic finite-state models are efficiently learnable from data, effective 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 finite-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 efficacy of such a translation model in the context of a call routing task of unconstrained speech utterances.

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