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

Abstract

This paper presents a translation model that is based on tree sequence alignment, where a tree sequence refers to a single sequence of sub-trees that covers a phrase. The model leverages on the strengths of both phrase-based and lin-guistically syntax-based method. It automati-cally learns aligned tree sequence pairs with mapping probabilities from word-aligned bi-parsed parallel texts. Compared with previous models, it not only captures non-syntactic phrases and discontinuous phrases with lin-guistically structured features, but also sup-ports multi-level structure reordering of tree typology with larger span. This gives our model stronger expressive power than other re-ported models. Experimental results on the NIST MT-2005 Chinese-English translation task show that our method statistically signifi-cantly outperforms the baseline systems. 1