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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 French-English 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 manually-created 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 effectiveness at reducing the size of the example corpus required.

Automated generalization of translation examples