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Learning Rules to Improve a Machine Translation System

  • David Kauchak
  • Charles Elkan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2837)

Abstract

In this paper we show how to learn rules to improve the performance of a machine translation system. Given a system consisting of two translation functions (one from language A to language B and one from B to A), training text is translated from A to B and back again to A. Using these two translations, differences in knowledge between the two translation functions are identified, and rules are learned to improve the functions. Context-independent rules are learned where the information suggests only a single possible translation for a word. When there are multiple alternate translations for a word, a likelihood ratio test is used to identify words that co-occur with each case significantly. These words are then used as context in context-dependent rules. Applied on the Pan American Health Organization corpus of 20,084 sentences, the learned rules improve the understandability of the translation produced by the SDL International engine on 78% of sentences, with high precision.

Keywords

Machine Translation Word List Ambiguity Resolution Translation System Context Word 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • David Kauchak
    • 1
  • Charles Elkan
    • 1
  1. 1.Department of Computer ScienceUniversity of CaliforniaSan Diego, La JollaUSA

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