All reviewers agreed that this paper make a nice contribution to NeurIPS by providing a novel general framework for generating calibrated surrogate loss functions for structured prediction problems. On the other hand, in discussion, they also stressed that including some baselines (e.g., SSVM/CRF+approximation/SPEN) in the experiments and reporting runtimes could make this paper much stronger. The authors should implement their promised changes in the camera-ready version.