2007. Analysis of statistical and morphological classes to generate weighted reordering hypotheses on a statistical machine translation system

One main challenge of statistical machine trans-lation (SMT) is dealing with word order. The main idea of the statistical machine reordering (SMR) approach is to use the powerful tech-niques of SMT systems to generate a weighted reordering graph for SMT systems. This tech-nique 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 in-crease of computational cost. The SMR approach is capable of generalizing re-orderings, which have been learned during train-ing, 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 Ofcial baseline system.