Paper: Syntax-Augmented Machine Translation using Syntax-Label Clustering

ACL ID D14-1019
Title Syntax-Augmented Machine Translation using Syntax-Label Clustering
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014

Recently, syntactic information has helped significantly to improve statistical ma- chine translation. However, the use of syn- tactic information may have a negative im- pact on the speed of translation because of the large number of rules, especially when syntax labels are projected from a parser in syntax-augmented machine translation. In this paper, we propose a syntax-label clus- tering method that uses an exchange algo- rithm in which syntax labels are clustered together to reduce the number of rules. The proposed method achieves clustering by directly maximizing the likelihood of synchronous rules, whereas previous work considered only the similarity of proba- bilistic distributions of labels. We tested the proposed method on Japanese-English and Chinese-English translation tasks an...