Coping with ambiguity in a large-scale machine translation system
Kathryn Baker, Alexander M. Franz, Pamela Jordan, Teruko Mitamura, Eric Nyberg · 1994
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.