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Creating an intelligent conversational system that understands vision andlanguage is one of the ultimate goals in Artificial Intelligence(AI)~\cite{winograd1972understanding}. Extensive research has focused onvision-to-language generation, however, limited research has touched oncombining these two modalities in a goal-driven dialog context. We propose amultimodal hierarchical reinforcement learning framework that dynamicallyintegrates vision and language for task-oriented visual dialog. The frameworkjointly learns the multimodal dialog state representation and the hierarchicaldialog policy to improve both dialog task success and efficiency. We alsopropose a new technique, state adaptation, to integrate context awareness inthe dialog state representation. We evaluate the proposed framework and thestate adaptation technique in an image guessing game and achieve promisingresults.

Multimodal Hierarchical Reinforcement Learning Policy for Task-Oriented Visual Dialog