Proceedings of the 9th Conference on Theoretical and Methodological Issues in Machine Translation (TMI-2002), pp. 31--41. Keihanna, Japan. March, 2002.
The automated translation of one natural language to another, known as machine translation, typically requires successful modeling of the grammars of the two languages and of the relationship between them. Rather than hand-coding these grammars and relationships, some machine translation efforts have begun to employ statistical methods, where the goal is to learn from a large amount of training examples of accurate translations. This work has also been extended to probabilistic finite-state approaches, most often via transducers. In this project, a novel combination of finite-state devices is employed. The model proposed, which consists of two probabilistically linked automata, is more flexible than a transducer model, giving increased ability to handle word order differences. In addition to the model and algorithms for its construction and use, we present several increased-coverage techniques, including methods for extracting partial results from the model. We present preliminary results for a test corpus of English to Spanish translations, which suggest the model may serve as a base for rudimentary translation, when used in conjunction with these extensions.
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Bibtex entry:
@inproceedings{DavisBrew:02,
author ={Davis, Paul C. and Chris Brew},
title ={{Stone Soup Translation}},
booktitle={Proceedings of the 9th Conference on Theoretical
and Methodological Issues in Machine Translation (TMI-2002)},
address ={Keihanna, Japan},
year ={2002},
pages ={31--41},
}