Paper: Fast Translation Rule Matching for Syntax-based Statistical Machine Translation

ACL ID D09-1108
Title Fast Translation Rule Matching for Syntax-based Statistical Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

In a linguistically-motivated syntax-based trans- lation system, the entire translation process is normally carried out in two steps, translation rule matching and target sentence decoding us- ing the matched rules. Both steps are very time- consuming due to the tremendous number of translation rules, the exhaustive search in trans- lation rule matching and the complex nature of the translation task itself. In this paper, we pro- pose a hyper-tree-based fast algorithm for trans- lation rule matching. Experimental results on the NIST MT-2003 Chinese-English translation task show that our algorithm is at least 19 times faster in rule matching and is able to help to save 57% of overall translation time over previ- ous methods when using large fragment transla- tion rules.