Forest-based tree sequence to string translation model

Abstract

This paper proposes a forest-based tree sequence to string translation model for syntax-based statistical machine translation, which automatically learns tree sequence to string translation rules from word-aligned source-side-parsed bilingual texts. The proposed model leverages on the strengths of both tree sequence-based and forest-based translation models. Therefore, it can not only utilize forest structure that compactly encodes exponential number of parse trees but also capture nonsyntactic translation equivalences with linguistically structured information through tree sequence. This makes our model potentially more robust to parse errors and structure divergence. Experimental results on the NIST MT-2003 Chinese-English translation task show that our method statistically significantly outperforms the four baseline systems.