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

We investigate the use of Fisher’s exact significance test for pruning the translation table of a hierarchical phrase-based statistical machine translation system. In addition to the significance values computed 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 models. Experimental results show that 1% to 2% BLEU improvements can be achieved along with substantial model size reduction in an Iraqi/English two-way translation task.