  Visual Dialog requires an agent to engage in a conversation with humans
grounded in an image. Many studies on Visual Dialog focus on the understanding
of the dialog history or the content of an image, while a considerable amount
of commonsense-required questions are ignored. Handling these scenarios depends
on logical reasoning that requires commonsense priors. How to capture relevant
commonsense knowledge complementary to the history and the image remains a key
challenge. In this paper, we propose a novel model by Reasoning with
Multi-structure Commonsense Knowledge (RMK). In our model, the external
knowledge is represented with sentence-level facts and graph-level facts, to
properly suit the scenario of the composite of dialog history and image. On top
of these multi-structure representations, our model can capture relevant
knowledge and incorporate them into the vision and semantic features, via
graph-based interaction and transformer-based fusion. Experimental results and
analysis on VisDial v1.0 and VisDialCK datasets show that our proposed model
effectively outperforms comparative methods.
