We investigate mutual benefits between syntax and semantic roles using neural network models, by studying a parsing-->SRL pipeline, a SRL-->parsing pipeline, and a simple joint model by embedding sharing. The integration of syntactic and semantic features gives promising results in a Chinese Semantic Tree-bank, demonstrating large potentials of neural models for joint parsing and semantic role labeling.