Toward Smaller, Faster, and Better Hierarchical Phrase-based SMT

We investigate the use of Fisher’s exact significance test for pruning the transla-tion table of a hierarchical phrase-based statistical machine translation system. In addition to the significance values com-puted by Fisher’s exact test, we introduce compositional properties to classify phrase pairs of same significance values. We also examine the impact of using significance values as a feature in translation mod-els. Experimental results show that 1 % to 2 % BLEU improvements can be achieved along with substantial model size reduc-tion in an Iraqi/English two-way transla-tion task.