Papers › Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation
Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation
Xiaohui Song, Longtao Huang, Hui Xue, Songlin Hu
Capturing emotions within a conversation plays an essential role in modern dialogue systems. However, the weak correlation between emotions and semantics brings many challenges to emotion recognition in conversation (ERC). Even semantically similar utterances, the emotion may vary drastically depending on contexts or speakers. In this paper, we propose a Supervised Prototypical Contrastive Learning (SPCL) loss for the ERC task. Leveraging the Prototypical Network, the SPCL targets at solving the imbalanced classification problem through contrastive learning and does not require a large batch size. Meanwhile, we design a difficulty measure function based on the distance between classes and introduce curriculum learning to alleviate the impact of extreme samples. We achieve state-of-the-art results on three widely used benchmarks. Further, we conduct analytical experiments to demonstrate the effectiveness of our proposed SPCL and curriculum learning strategy. We release the code at https://github.com/caskcsg/SPCL.
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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 | EmoryNLP | SPCL-CL-ERC | Weighted-F1 | 40.94 | #4 of 28 | Archive leaderboard | report |
| Emotion Recognition in Conversation | IEMOCAP | SPCL-CL-ERC | Weighted-F1 | 69.74 | #19 of 59 | Archive leaderboard | report |
| Emotion Recognition in Conversation | MELD | SPCL-CL-ERC | Weighted-F1 | 67.25 | #9 of 68 | 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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