A Joint Rule Selection Model for Hierarchical Phrase-Based Translation

In hierarchical phrase-based SMT systems, statistical models are integrated to guide the hierarchical rule selection for better translation performance. Previous work mainly focused on the selection of either the source side of a hierarchical rule or the target side of a hierarchical rule rather than considering both of them simultaneously. This paper presents a joint model to predict the selection of hierarchical rules. The proposed model is estimated based on four sub-models where the rich context knowledge from both source and target sides is leveraged. Our method can be easily incorporated into the practical SMT systems with the log-linear model framework. The experimental results show that our method can yield significant improvements in performance.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Lei Cui, Dongdong Zhang, Mu Li, Ming Zhou, Tiejun Zhao
• Keywords: ACL 2010, Computational Linguistics, Hierarchical Phrase-based Smt, Hierarchical Rules, Smt Systems