Maximum entropy based rule selection model for syntax-based statistical machine translation
Qun Liu, Zhongjun He, Yang Liu, Shouxun Lin · 2008
This paper proposes a novel maximum entropy based rule selection (MERS) model for syntax-based statistical machine translation (SMT). The MERS model combines local contextual information around rules and information of sub-trees covered by variables in rules. Therefore, our model allows the decoder to perform context-dependent rule selection during decoding. We incorporate the MERS model into a state-of-the-art linguistically syntax-based SMT model, the tree-to-string alignment template model. Experiments show that our approach achieves significant improvements over the baseline system.