Coping with ambiguity in a large-scale machine translation system

Authors:
Kathryn L. Baker;Alexander M. Franz;Pamela W. Jordan;Teruko Mitamura;Eric H. Nyberg
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Year:
1994

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Abstract

In an interlingual knowledge-based machine translation system, ambignuity arises when the source language analyzer produces more than one interlingua expression for a source sentence. This can have a negative impact on translation quality, since a target sentence may be produced from an unintended meaning. In this paper we describe the methods used in the KANT machine translation system to reduce or eliminate ambiguity in a large-scale application domain. We also test these methods on a large corpus of test sentences, in order to illustrate how the different disambiguation methods reduce the average number of parses per sentence.