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Clustering-based Statistical Machine Translation Using Syntactic Structure and Word Similarity  

Kim, Han-Kyong (포항공과대학교 정보처리학과)
Na, Hwi-Dong (포항공과대학교 컴퓨터공학과)
Li, Jin-Ji (포항공과대학교 컴퓨터공학과)
Lee, Jong-Hyeok (포항공과대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.37, no.4, 2010 , pp. 297-304 More about this Journal
Abstract
Clustering method which based on sentence type or document genre is a technique used to improve translation quality of SMT(statistical machine translation) by domain-specific translation. But there is no previous research using sentence type and document genre information simultaneously. In this paper, we suggest an integrated clustering method that classifying sentence type by syntactic structure similarity and document genre by word similarity information. We interpolated domain-specific models from clusters with general models to improve translation quality of SMT system. Kernel function and cosine measures are applied to calculate structural similarity and word similarity. With these similarities, we used machine learning algorithms similar to K-means to clustering. In Japanese-English patent translation corpus, we got 2.5% point relative improvements of translation quality at optimal case.
Keywords
SMT(Statistical Machine Translation); Clustering; Domain-specific model; Syntactic Structural similarity; word similarity;
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Times Cited By KSCI : 1  (Citation Analysis)
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