Learning local word reorderings for hierarchical phrase-based statistical machine translation

Statistical models for reordering source words have been used to enhance hierarchical phrase-based statistical machine translation. There are existing word-reordering models that learn reorderings for any two source words in a sentence or only for two contiguous 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 distances less than a specific threshold are useful to improve translation quality. Compared with previous work, our method more effectively and efficiently exploits helpful word-reordering information; it improves a basic hierarchical phrase-based system by 2.4-3.1 BLEU points and keeps the average time of translating one sentence under 10 s.