  To encourage AI agents to conduct meaningful Visual Dialogue (VD), the use of
Reinforcement Learning has been proven potential. In Reinforcement Learning, it
is crucial to represent states and assign rewards based on the action-caused
transitions of states. However, the state representation in previous Visual
Dialogue works uses the textual information only and its transitions are
implicit. In this paper, we propose Explicit Concerning States (ECS) to
represent what visual contents are concerned at each round and what have been
concerned throughout the Visual Dialogue. ECS is modeled from multimodal
information and is represented explicitly. Based on ECS, we formulate two
intuitive and interpretable rewards to encourage the Visual Dialogue agents to
converse on diverse and informative visual information. Experimental results on
the VisDial v1.0 dataset show our method enables the Visual Dialogue agents to
generate more visual coherent, less repetitive and more visual informative
dialogues compared with previous methods, according to multiple automatic
metrics, human study and qualitative analysis.
