Paper: Tree Kernel-based SVM with Structured Syntactic Knowledge for BTG-based Phrase Reordering

ACL ID D09-1073
Title Tree Kernel-based SVM with Structured Syntactic Knowledge for BTG-based Phrase Reordering
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

Structured syntactic knowledge is important for phrase reordering. This paper proposes us- ing convolution tree kernel over source parse tree to model structured syntactic knowledge for BTG-based phrase reordering in the con- text of statistical machine translation. Our study reveals that the structured syntactic fea- tures over the source phrases are very effective for BTG constraint-based phrase reordering and those features can be well captured by the tree kernel. We further combine the structured features and other commonly-used linear fea- tures into a composite kernel. Experimental re- sults on the NIST MT-2005 Chinese-English translation tasks show that our proposed phrase reordering model statistically signifi- cantly outperforms the baseline methods.