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Automatic Rule Learning for Resource-Limited MT

Part of the Lecture Notes in Computer Science book series (LNAI,volume 2499)


Machine Translation of minority languages presents unique challenges, including the paucity of bilingual training data and the unavailability of linguistically-trained speakers. This paper focuses on a machine learning approach to transfer-based MT, where data in the form of translations and lexical alignments are elicited from bilingual speakers, and a seeded version-space learning algorithm formulates and refines transfer rules. A rule-generalization lattice is defined based on LFG-style f-structures, permitting generalization operators in the search for the most general rules consistent with the elicited data. The paper presents these methods and illustrates examples.


  • Machine Translation
  • Relative Clause
  • Target Language
  • Minority Language
  • Source Language

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  • DOI: 10.1007/3-540-45820-4_1
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© 2002 Springer-Verlag Berlin Heidelberg

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Carbonell, J. et al. (2002). Automatic Rule Learning for Resource-Limited MT. In: Richardson, S.D. (eds) Machine Translation: From Research to Real Users. AMTA 2002. Lecture Notes in Computer Science(), vol 2499. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44282-0

  • Online ISBN: 978-3-540-45820-3

  • eBook Packages: Springer Book Archive