Learning Word Reorderings for Hierarchical Phrase-based Statistical Machine Translation

Statistical models for reordering source words have been used to enhance the hierarchical phrase-based statistical machine translation system. Existing word reordering models learn the reordering for any two source words in a sentence or only for two continuous words. This paper proposes a series of separate sub-models to learn reorderings for word pairs with different distances. Our experiments demonstrate that reordering sub-models for word pairs with distance less than a specific threshold are useful to improve translation quality. Compared with previous work, our method may more effectively and efficiently exploit helpful word reordering information.