A finite-state approach to machine translation

The problem of machine translation can be viewed as consisting of two subproblems: (a) lexical selection; (b) lexical reordering. We propose stochastic finite-state models for these two subproblems. Stochastic finite-state models are efficiently able to learn 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 of 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.