Automated generalization of translation examples

  • Authors:
  • Ralf D. Brown

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
  • Year:
  • 2000

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Abstract

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.