A Generalized Reordering Model for Phrase-Based Statistical Machine Translation

Phrase-based translation models are widely studied in statistical machine translation (SMT). However, the existing phrase-based translation models either can not deal with non-contiguous phrases or reorder phrases only by the rules without an effective reordering model. In this paper, we propose a generalized reordering model (GREM) for phrase-based statistical machine translation, which is not only able to capture the knowledge on the local and global reordering of phrases, but also is able to obtain some capabilities of phrasal generalization by using non-contiguous phrases. The experimental results have indicated that our model outperforms MEBTG (enhanced BTG with a maximum entropy-based reordering model) and HPTM (hierarchical phrase-based translation model) by improvement of 1.54% and 0.66% in BLEU.