A Tree Sequence Alignment-based Tree-to-Tree Translation Model

This paper presents a translation model that is based on tree sequence alignment, where a tree sequence refers to a single sequence of subtrees that covers a phrase. The model leverages on the strengths of both phrase-based and linguistically syntax-based method. It automatically learns aligned tree sequence pairs with mapping probabilities from word-aligned biparsed parallel texts. Compared with previous models, it not only captures non-syntactic phrases and discontinuous phrases with linguistically structured features, but also supports multi-level structure reordering of tree typology with larger span. This gives our model stronger expressive power than other reported models. Experimental results on the NIST MT-2005 Chinese-English translation task show that our method statistically significantly outperforms the baseline systems.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Min Zhang, Hongfei Jiang, AiTi Aw, Haizhou Li, Chew Lim Tan, Sheng Li
• Keywords: ACL 2008, Computational Linguistics, Tree Sequence, Tree Sequence Alignment, Tree Sequence Pairs