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ECML
2003
Springer

Learning Rules to Improve a Machine Translation System

13 years 8 months ago
Learning Rules to Improve a Machine Translation System
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.
David Kauchak, Charles Elkan
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where ECML
Authors David Kauchak, Charles Elkan
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