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Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation

2022-07-05 05:54:20
Bin Li, Yixuan Weng, Ziyu Ma, Bin Sun, Shutao Li
arXiv_CV
         
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2207.01823

PDF

https://arxiv.org/pdf/2207.01823.pdf

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