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

Structured syntactic knowledge is important for phrase reordering. This paper proposes using convolution tree kernel over source parse tree to model structured syntactic knowledge for BTG-based phrase reordering in the context of statistical machine translation. Our study reveals that the structured syntactic features 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 features into a composite kernel. Experimental results on the NIST MT-2005 Chinese-English translation tasks show that our proposed phrase reordering model statistically significantly outperforms the baseline methods.