Question answering is an effective method for obtaining information from knowledge bases (KB). In this paper, we propose NS-CQA, a data-efficient reinforcement learning framework complex question by using only modest number of training samples. Our consists neural generator and symbolic executor that, respectively, transforms natural-language into sequence primitive actions, executes them over ...
