A Finite-State Approach to Machine Translation (2001)
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| Venue: | In Proc. of the North American Chapter of the Association for Computational Linguistics |
| Citations: | 20 - 1 self |
BibTeX
@INPROCEEDINGS{Bangalore01afinite-state,
author = {Srinivas Bangalore and Giuseppe Riccardi},
title = {A Finite-State Approach to Machine Translation},
booktitle = {In Proc. of the North American Chapter of the Association for Computational Linguistics},
year = {2001}
}
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Abstract
The problem of machine translation can be viewed as consisting of two subproblems (a) Lexical Selection and (b) Lexical Reordering. We propose stochas- tic 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 finitestate 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 utter- ances.
