  This paper introduces the schemes of Team LingJing's experiments in
NLPCC-2022-Shared-Task-4 Multi-modal Dialogue Understanding and Generation
(MDUG). The MDUG task can be divided into two phases: multi-modal context
understanding and response generation. To fully leverage the visual information
for both scene understanding and dialogue generation, we propose the
scene-aware prompt for the MDUG task. Specifically, we utilize the
multi-tasking strategy for jointly modelling the scene- and session-
multi-modal understanding. The visual captions are adopted to aware the scene
information, while the fixed-type templated prompt based on the scene- and
session-aware labels are used to further improve the dialogue generation
performance. Extensive experimental results show that the proposed method has
achieved state-of-the-art (SOTA) performance compared with other competitive
methods, where we rank the 1-st in all three subtasks in this MDUG competition.
