Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Tree-State Based Rule Selection Models for Hierarchical Phrase-Based Machine Translation / 2817
Shujian Huang, Huifeng Sun, Chengqi Zhao, Jinsong Su, Xin-Yu Dai, Jiajun Chen

Hierarchical phrase-based translation systems (HPBs) perform translation using a synchronous context free grammar which has only one unified non-terminal for every translation rule. While the usage of the unified non-terminal brings freedom to generate translations with almost arbitrary structures, it also takes the risks of generating low-quality translations which has a wrong syntactic structure. In this paper, we propose tree-state models to discriminate the good or bad usage of translation rules based on the syntactic structures of the source sentence. We propose to use statistical models and context dependent features to estimate the probability of each tree state for each translation rule and punish the usage of rules in the translation system which violates their tree states. Experimental results demonstrate that these simple models could bring significant improvements to the translation quality.