Carnegie Mellon University
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Automatic Rule Learning for Resource-Limited MT

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journal contribution
posted on 2005-01-01, 00:00 authored by Jaime G. Carbonell, Katharina Probst, Erik Peterson, Christian Monson, Alon LavieAlon Lavie, Ralf D Brown, Lorraine LevinLorraine Levin
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.




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