A Lexicalized Reordering Model for Hierarchical Phrase-based Translation
Hailong Cao, Dongdong Zhang, Mu Li, Ming Quan Zhou, Tiejun Zhao · 2014
Lexicalized reordering model plays a central role in phrase-based statistical machine translation sys-tems. The reordering model specifies the orientation for each phrase and calculates its probability con-ditioned on the phrase. In this paper, we describe the necessity and the challenge of introducing such a reordering model for hierarchical phrase-based translation. To deal with the challenge, we propose a novel lexicalized reordering model which is built directly on synchronous rules. For each target phrase contained in a rule, we calculate its orientation probability conditioned on the rule. We test our model on both small and large scale data. On NIST machine translation test sets, our reordering model achieved a 0.6-1.2 BLEU point improvements for Chinese-English translation over a strong baseline hierarchical phrase-based system. 1