Convolution tree kernels are an efficient and effective method for comparing syntac- tic structures in NLP methods. However, current kernel methods such as subset tree kernel and partial tree kernel understate the similarity of very similar tree structures. Although soft-matching approaches can im- prove the similarity scores, they are corpus- dependent and match relaxations may be task-specific. We propose an alternative ap- proach called descending path kernel which gives intuitive similarity scores on compa- rable structures. This method is evaluated on two temporal relation extraction tasks and demonstrates its advantage over rich syntactic representations.