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End-to-end learning framework is useful for building dialog systems for its simplicity in training and efficiency in model updating. However, current end-to-end approaches only consider user semantic inputs in learning and under-utilize other user information. Therefore, we propose to include user sentiment obtained through multimodal information (acoustic, dialogic and textual), in the end-to-end learning framework to make systems more user-adaptive and effective. We incorporated userdoi:10.18653/v1/p18-1140 dblp:conf/acl/YuS18 fatcat:g6qahcdmcregvkrvf4b4ntzyhu