Analysis of statistical and morphological classes to generate weighted reordering hypotheses on a statistical machine translation system

One main challenge of statistical machine translation (SMT) is dealing with word order. The main idea of the statistical machine reordering (SMR) approach is to use the powerful techniques of SMT systems to generate a weighted reordering graph for SMT systems. This technique supplies reordering constraints to an SMT system, using statistical criteria. In this paper, we experiment with different graph pruning which guarantees the translation quality improvement due to reordering at a very low increase of computational cost. The SMR approach is capable of generalizing reorderings, which have been learned during training, by using word classes instead of words themselves. We experiment with statistical and morphological classes in order to choose those which capture the most probable reorderings. Satisfactory results are reported in the WMT07 Es/En task. Our system outperforms in terms of BLEU the WMT07 Official baseline system.