Hierarchical Phrase-based Machine Translation with Word-based Reordering Model

Hierarchical phrase-based machine translation can capture global reordering with synchronous context-free grammar, but has little ability to evaluate the correctness of word orderings during decoding. We propose a method to integrate word-based reordering model into hierarchical phrasebased machine translation to overcome this weakness. Our approach extends the synchronous context-free grammar rules of hierarchical phrase-based model to include reordered source strings, allowing efficient calculation of reordering model scores during decoding. Our experimental results on Japanese-to-English basic travel expression corpus showed that the BLEU scores obtained by our proposed system were better than those obtained by a standard hierarchical phrase-based machine translation system.