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Reordering poses a major challenge in machine translation (MT) between twolanguages with significant differences in word order. In this paper, we presenta novel reordering approach utilizing sparse features based on dependency wordpairs. Each instance of these features captures whether two words, which arerelated by a dependency link in the source sentence dependency parse tree,follow the same order or are swapped in the translation output. Experiments onChinese-to-English translation show a statistically significant improvement of1.21 BLEU point using our approach, compared to a state-of-the-art statisticalMT system that incorporates prior reordering approaches.

To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation