In this work we present a novel technique to rescore fragments in the Data-Oriented Translation model based on their contribution to translation accuracy. We describe three new rescoring methods, and present the initial results of a pilot experiment on a small subset of the Europarl corpus. This work is a proof-of-concept, and is the first step in directly optimizing translation decisions solely on the hypothesized accuracy of potential translations resulting from those decisions. 1

We propose Bilingual Tree Kernels (BTKs) to capture the structural similarities across a pair of syntactic translational equivalences and apply BTKs to sub-tree alignment along with some plain features. Our study reveals that the structural features embedded in a bilingual parse tree pair are very effective for sub-tree alignment and the bilingual tree kernels can well capture such features. The experimental results show that our approach achieves a significant improvement on both gold standard tree bank and automatically parsed tree pairs against a heuristic similarity based method. We further apply the sub-tree alignment in machine translation with two methods. It is suggested that the subtree alignment benefits both phrase and syntax based systems by relaxing the constraint of the word alignment. 1

We employ Maximum Entropy model to conduct sub-tree alignment between bilingual phrasal structure trees. Various lexical and structural knowledge is explored to measure the syntactic similarity across Chinese-English bilingual tree pairs. In the experiment, we evaluate the sub-tree alignment using both gold standard tree bank and the automatically parsed corpus with manually annotated sub-tree alignment. Compared with a heuristic similarity based method, the proposed method significantly improves the performance with only limited sub-tree aligned data. To examine its effectiveness for multilingual applications, we further attempt different approaches to apply the sub-tree alignment in both phrase and syntax based SMT systems. We then compare the performance with that of the widely used word alignment. Experimental results on benchmark data show that sub-tree alignment benefits both systems by relaxing the constraint of the word alignment.