Learning semantic representations for nonterminals in hierarchical phrase-based translation

In hierarchical phrase-based translation, coarse-grained nonterminal Xs may generate inappropriate translations due to the lack of sufficient information for phrasal substitution. In this paper we propose a framework to refine nonterminals in hierarchical translation rules with real-valued semantic representations. The semantic representations are learned via a weighted mean value and a minimum distance method using phrase vector representations obtained from large scale monolingual corpus. Based on the learned semantic vectors, we build a semantic nonterminal refinement model to measure semantic similarities between phrasal substitutions and nonterminal Xs in translation rules. Experiment results on Chinese-English translation show that the proposed model significantly improves translation quality on NIST test sets.