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To enable collaboration and communication between humans and agents, this paper investigates learning to acquire commonsense evidence for action justification. In particular, we have developed an approach based on the generative Conditional Variational Autoencoder (CVAE) that models object relations/attributes of the world as latent variables and jointly learns a performer that predicts actions and an explainer that gathers commonsense evidence to justify the action. Our empirical results havedoi:10.18653/v1/d18-1283 dblp:conf/emnlp/YangGSC18 fatcat:tujk3fb32jdptfzw6vythpalay