Using features from topic models to alleviate over-generation in hierarchical phrase-based translation

In hierarchical phrase-based translation systems, the grammars (SCFG rules) have over-generation problem because we can replace the non-terminalX with almost everything without knowing the syntactic or semantic role ofX. In this paper, we present an approach that uses topic models to learn the distributions for non-terminals in each SCFG rule, based on which we further derive static features for the discriminative framework of statistical machine translation. Experimental results on three corpora show that we can obtain some gains in BLEU by using these features derived from topic models to alleviate the overgeneration problem in hierarchical phrase-based translation. Copyright © 2011 ISCA.