Automated Generalization of Translation Examples

Previous work has shown that adding generalization of the examples in the corpus of an example-based machine translation (EBMT) system can reduce the required amount of pretranslated example text by as much as an order of magnitude for Spanish-English and FrenchEnglish EBMT. Using word clustering to automatically generalize the example corpus can provide the majority of this improvement for French-English with no manual intervention; the prior work required a large bilingual dictionary tagged with parts of speech and the manual creation of grammar rules. By seeding the clustering with a small amount of manuallycreated information, even better performance can be achieved. This paper describes a method whereby bilingual word clustering can be performed using standard monolingual document clustering techniques, and its e ectiveness at reducing the size of the example corpus required.