Maximum Entropy based Rule Selection Model for Syntax-based Statistical Machine Translation

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 treeto-string alignment template model. Experiments show that our approach achieves significant improvements over the baseline system.