To enhance the reordering capacity of the phrase-based SMT (statistical machine translation), the study leverages the head-modifier dependency structure on the source to model the reordering. The model is added to baseline model in the form of soft-constraint way. The proposed model explores an approach to utilize the constituent based parse tree that the parse tree is mapped into sets of head-modifier relationships. Experimental results show that this model improves the local reordering significantly.