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