Reordering Modeling using Weighted Alignment Matrices.

In most statistical machine translation systems, the phrase/rule extraction algorithm uses alignments in the 1-best form, which might contain spurious alignment points. The usage of weighted alignment matrices that encode all possible alignments has been shown to generate better phrase tables for phrase-based systems. We propose two algorithms to generate the well known MSD reordering model using weighted alignment matrices. Experiments on the IWSLT 2010 evaluation datasets for two language pairs with different alignment algorithms show that our methods produce more accurate reordering models, as can be shown by an increase over the regular MSD models of 0.4 BLEU points in the BTEC French to English test set, and of 1.5 BLEU points in the DIALOG Chinese to English test set.