Linguistically Annotated BTG for Statistical Machine Translation

Bracketing Transduction Grammar (BTG) is a natural choice for effective integration of desired linguistic knowledge into sta-tistical machine translation (SMT). In this paper, we propose a Linguistically Anno-tated BTG (LABTG) for SMT. It conveys linguistic knowledge of source-side syn-tax structures to BTG hierarchical struc-tures through linguistic annotation. From the linguistically annotated data, we learn annotated BTG rules and train linguisti-cally motivated phrase translation model and reordering model. We also present an annotation algorithm that captures syntac-tic information for BTG nodes. The ex-periments show that the LABTG approach significantly outperforms a baseline BTG-based system and a state-of-the-art phrase-based system on the NIST MT-05 Chinese-to-English translation task. Moreover, we empirically demonstrate that the proposed method achieves better translation selec-tion and phrase reordering.