Disentangled Dual-Branch Graph Learning for Conversational Emotion Recognition
Multimodal emotion recognition in conversations aims to infer utterance-level emotions by jointly modeling textual, acoustic, and visual cues within context. Despite recent progress, key challenges remain, including redundant cross-modal information, imperfect semantic alignment, and insufficient modeling of high-order speaker interactions. To address these issues, we propose a framework that combines dual-space feature disentanglement with dual-branch graph learning. A shared encoder and modali
Record details
Published: 3 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (3 April 2026), “Disentangled Dual-Branch Graph Learning for Conversational Emotion Recognition,” evidence record 6392, https://ethics.ai/record/6392 (originally published by arXiv).
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