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Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in Conversation
Feiyu Chen, Jie Shao, Shuyuan Zhu, Heng Tao Shen
Complex relationships of high arity across modality and context dimensions is a critical challenge in the Emotion Recognition in Conversation (ERC) task. Yet, previous works tend to encode multimodal and contextual relationships in a loosely-coupled manner, which may harm relationship modelling. Recently, Graph Neural Networks (GNN) which show advantages in capturing data relations, offer a new solution for ERC. However, existing GNN-based ERC models fail to address some general limits of GNNs, including assuming pairwise formulation and erasing high-frequency signals, which may be trivial for many applications but crucial for the ERC task. In this paper, we propose a GNN-based model that explores multivariate relationships and captures the varying importance of emotion discrepancy and commonality by valuing multi-frequency signals. We empower GNNs to better capture the inherent relationships among utterances and deliver more sufficient multimodal and contextual modelling. Experimental results show that our proposed method outperforms previous state-of-the-art works on two popular multimodal ERC datasets.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Emotion Recognition in Conversation | CMU-MOSEI-Sentiment | M3Net | Accuracy | 43.67 | #5 of 7 | Archive leaderboard | report |
| Emotion Recognition in Conversation | CMU-MOSEI-Sentiment | M3Net | Weighted F1 | 41.12 | #5 of 7 | Archive leaderboard | report |
| Emotion Recognition in Conversation | IEMOCAP-4 | M3Net | Accuracy | 83.67 | #4 of 8 | Archive leaderboard | report |
| Emotion Recognition in Conversation | IEMOCAP-4 | M3Net | Weighted F1 | 83.57 | #4 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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