{
  "id": 6392,
  "url": "https://arxiv.org/abs/2604.14204v1",
  "title": "Disentangled Dual-Branch Graph Learning for Conversational Emotion Recognition",
  "summary": "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",
  "authors": "Chengling Guo, Yuntao Shou, Tao Meng, Wei Ai, Yun Tan, Keqin Li",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-03T14:47:26.000Z",
  "fetched_at": "2026-07-14T16:32:28.609Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6392",
  "original_url": "https://arxiv.org/abs/2604.14204v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}