2023/12/31 by Shijue Huang, Huang, Shijue, Libo Qin +7 · 6 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Speech and dialogue systems #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2401.00424
openalex publication_date 2023/12/31 · openalex created_date 2024/01/03 · openalex updated_date 2026/07/28
Multi-modal intent detection aims to utilize various modalities to understand the user's intentions, which is essential for the deployment of dialogue systems in real-world scenarios. The two core challenges for multi-modal intent detection are (1) how to effectively align and fuse different features of modalities and (2) the limited labeled multi-modal intent training data. In this work, we introduce a shallow-to-deep interaction framework with data augmentation (SDIF-DA) to address the above challenges. Firstly, SDIF-DA leverages a shallow-to-deep interaction module to progressively and effectively align and fuse features across text, video, and audio modalities. Secondly, we propose a ChatGPT-based data augmentation approach to automatically augment sufficient training data. Experimental results demonstrate that SDIF-DA can effectively align and fuse multi-modal features by achieving state-of-the-art performance. In addition, extensive analyses show that the introduced data augmentation approach can successfully distill knowledge from the large language model.