  Generating dialogue grounded in videos requires a high level of understanding
and reasoning about the visual scenes in the videos. However, existing large
visual-language models are not effective due to their latent features and
decoder-only structure, especially with respect to spatio-temporal relationship
reasoning. In this paper, we propose a novel approach named MSG-BART, which
enhances the integration of video information by incorporating a
multi-granularity spatio-temporal scene graph into an encoder-decoder
pre-trained language model. Specifically, we integrate the global and local
scene graph into the encoder and decoder, respectively, to improve both overall
perception and target reasoning capability. To further improve the information
selection capability, we propose a multi-pointer network to facilitate
selection between text and video. Extensive experiments are conducted on three
video-grounded dialogue benchmarks, which show the significant superiority of
the proposed MSG-BART compared to a range of state-of-the-art approaches.
