Learning Semantic Representations for Nonterminals in Hierarchical Phrase-Based Translation

Abstract

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 ChineseEnglish translation show that the proposed model significantly improves translation quality on NIST test sets.