  Reordering poses a major challenge in machine translation (MT) between two
languages with significant differences in word order. In this paper, we present
a novel reordering approach utilizing sparse features based on dependency word
pairs. Each instance of these features captures whether two words, which are
related by a dependency link in the source sentence dependency parse tree,
follow the same order or are swapped in the translation output. Experiments on
Chinese-to-English translation show a statistically significant improvement of
1.21 BLEU point using our approach, compared to a state-of-the-art statistical
MT system that incorporates prior reordering approaches.
