Multiple Strategies for Automatic Disambiguation in Technical Translation

The use of knowledge-based machine translation with controlled technical text can produce high-quality translations. However, building and maintaining knowledge bases can require significant time and effort, since they typically involve handcoding of semantic preferences. When a system can't disambiguate based on semantic preferences, it can initiate interactive disambiguation with the author to improve the likelihood of an accurate translation, but this decreases the productivity of text authoring. In this paper, we present an experimental evaluation of automatic disambiguation strategies which could eliminate the need for interactive structural disambiguation in the KANT machine translation system.