ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generation

• URL: http://arxiv.org/abs/2308.00400v2
• Date: Wed, 2 Aug 2023 13:53:30 GMT
• Title: ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generation
• Authors: Bo Zhang, Jian Wang, Hui Ma, Bo Xu, and Hongfei Lin
• Abstract summary: ZRIGF implements a two-stage learning strategy, comprising contrastive pre-training and generative pre-training. Comprehensive experiments conducted on both text-based and image-grounded dialogue datasets demonstrate ZRIGF's efficacy in generating contextually pertinent and informative responses.
• Score: 17.310200022696016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-grounded dialogue systems benefit greatly from integrating visual information, resulting in high-quality response generation. However, current models struggle to effectively utilize such information in zero-resource scenarios, mainly due to the disparity between image and text modalities. To overcome this challenge, we propose an innovative multimodal framework, called ZRIGF, which assimilates image-grounded information for dialogue generation in zero-resource situations. ZRIGF implements a two-stage learning strategy, comprising contrastive pre-training and generative pre-training. Contrastive pre-training includes a text-image matching module that maps images and texts into a unified encoded vector space, along with a text-assisted masked image modeling module that preserves pre-training visual features and fosters further multimodal feature alignment. Generative pre-training employs a multimodal fusion module and an information transfer module to produce insightful responses based on harmonized multimodal representations. Comprehensive experiments conducted on both text-based and image-grounded dialogue datasets demonstrate ZRIGF's efficacy in generating contextually pertinent and informative responses. Furthermore, we adopt a fully zero-resource scenario in the image-grounded dialogue dataset to demonstrate our framework's robust generalization capabilities in novel domains. The code is available at https://github.com/zhangbo-nlp/ZRIGF.

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