This paper proposes a novel lexicalized approach for rule selection for syntax-based statistical machine translation (SMT). We build maximum entropy (MaxEnt) models which combine rich context information for selecting translation rules during decoding. We successfully integrate the MaxEnt-based rule selection models into the state-of-the-art syntax-based SMT model. Experiments show that our lexicalized approach for rule selection achieves statistically significant improvements over the state-of-the-art SMT system.
Besides, REF built a maximum entropy model which combines rich context information for selecting translation rules during decoding.
Improving Statistical Machine Translation using Lexicalized Rule Selection
International Conference On Computational Linguistics
Computer Science