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Computer Science @ Rochester
Friday, April 27, 2007
10:30 AM
CBS Room 601
Ph.D. Thesis Proposal
Matt Post
University of Rochester
Syntax-based language models for machine translation
After a decade of research, syntax-based statistical machine translation is beginning to see performance on par with the best phrase-based systems. Somewhat surprisingly, these gains have been accomplished at both ends of the linguistic spectrum: One class of approaches makes extensive use of linguistic annotation, while the other uses only a single generic nonterminal in a synchronous grammar framework. Common to all approaches along this spectrum, however, is the research focus on syntax-based *translation* models, while maintaining the use of n-grams in decoding algorithms. Apart from a few half-hearted attempts at using parsers as language models, there has been no research in using syntax in language models for machine translation.

N-grams are widely acknowledged to be poor models of language. In this thesis proposal, we present the case against them, and suggest that parsers will also make poor language models for machine translation. We propose the investigation and development of new syntax-based language models and outline a plan for accomplishing this over the next few years.