Refinements in BTG-based Statistical Machine Translation
Deyi Xiong, M. Zhang, Ai Ti Aw, Haitao Mi, Qun Liu, Shouxun Lin · 2008
Bracketing Transduction Grammar (BTG) has been well studied and used in statistical machine translation (SMT) with promising results. However, there are two major issues for BTG-based SMT. First, there is no effec-tive mechanism available for predicting or-ders between neighboring blocks in the orig-inal BTG. Second, the computational cost is high. In this paper, we introduce two re-finements for BTG-based SMT to achieve better reordering and higher-speed decod-ing, which include (1) reordering heuristics to prevent incorrect swapping and reduce search space, and (2) special phrases with tags to indicate sentence beginning and end-ing. The two refinements are integrated into a well-established BTG-based Chinese-to-English SMT system that is trained on large-scale parallel data. Experimental results on the NIST MT-05 task show that the proposed refinements contribute significant improve-ment of 2 % in BLEU score over the baseline system. 1