Hybrid Relational Graphs with Sentiment-Laden Semantic Alignment for Multimodal Emotion Recognition in Conversation

Abstract

Multimodal Emotion Recognition in Conversation (MERC) focuses on detecting the emotions expressed by speakers in each utterance. Recent research has increasingly leveraged graph-based models to capture interactive relationships in conversations, enhancing the ability to extract emotional cues. However, existing methods primarily focus on explicit utterance-level relationships, neglecting both the implicit connections within individual modality and the differences in implicit relationships across modalities. Moreover, these methods often overlook the role of sentimental features in conversation history in cross-modal semantic alignment. To address these issues, we propose a novel model that employs modality-adaptive hybrid relational graphs to enrich the dialogue graph by inferring implicit relationships between nodes within each modality. Furthermore, we introduce historical sentiment through a progressive strategy that utilizes contrastive learning to refine cross-modal semantic alignment. Experimental results demonstrate the superior performance of our approach over state-of-the-art methods on the IEMOCAP and MELD datasets. Our code is available at https://github.com/cgao-comp/HRG-SSA.