Coping With Ambiguity in a Large-Scale Machine Translation System

In an interlingual knowledge-based machine translation system, ambiguity 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.

• Type: Conference
• Year: 1994
• Where: COLING
• Authors: Kathryn L. Baker, Alexander Franz, Pamela W. Jordan, Teruko Mitamura, Eric Nyberg
• Keywords: COLING 1994, COLING 2008, Interlingual Knowledge-based Machine, Machine Translation System, Source Sentence