Information-based machine translation

Authors:
Keiko Horiguchi
Affiliations:
Spoken Language Technology, San Jose, CA
Venue:
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Year:
2001

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Abstract

This paper describes an approach to Machine Translation that places linguistic information at its foundation. The difficulty of translation from English to Japanese is illustrated with data that shows the influence of various linguistic contextual factors. Next, a method for natural language transfer is presented that integrates translation examples (represented as typed feature structures with source-target indices) with linguistic rules and constraints. The method has been implemented, and the results of an evaluation are presented.