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. 1 Introduction The KANT system [Mitamura et al., 1991] is a system for Knowledge-based, Accurate Natural-language Translation. The system is used in focused technical domains for multilingual translation of controlled source language documents. KANT is an interlingua-based system: the source language analyzer produces an interlingua expression for each source..