TopicDiff: A Topic-enriched Diffusion Approach for Multimodal Conversational Emotion Detection

TL;DR

This paper introduces TopicDiff, a diffusion-based neural topic model that enhances multimodal conversational emotion detection by effectively capturing topic information across acoustic, visual, and language modalities, leading to improved performance.

Contribution

It proposes a novel diffusion-enhanced neural topic model for multimodal emotion detection, addressing the diversity deficiency in traditional neural topic models and integrating multimodal topic information.

Findings

TopicDiff significantly outperforms state-of-the-art baselines.

Multimodal topic information improves emotion detection accuracy.

Acoustic and vision topics are more discriminative than language.

Abstract

Multimodal Conversational Emotion (MCE) detection, generally spanning across the acoustic, vision and language modalities, has attracted increasing interest in the multimedia community. Previous studies predominantly focus on learning contextual information in conversations with only a few considering the topic information in single language modality, while always neglecting the acoustic and vision topic information. On this basis, we propose a model-agnostic Topic-enriched Diffusion (TopicDiff) approach for capturing multimodal topic information in MCE tasks. Particularly, we integrate the diffusion model into neural topic model to alleviate the diversity deficiency problem of neural topic model in capturing topic information. Detailed evaluations demonstrate the significant improvements of TopicDiff over the state-of-the-art MCE baselines, justifying the importance of multimodal topic information to MCE and the effectiveness of TopicDiff in capturing such information. Furthermore, we observe an interesting finding that the topic information in acoustic and vision is more discriminative and robust compared to the language.