Papers › EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion...
EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation
Yingjian Liu, Jiang Li, XiaoPing Wang, Zhigang Zeng
Emotion Recognition in Conversation (ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper, we propose an emotional inertia and contagion-driven dependency modeling approach (EmotionIC) for ERC task. Our EmotionIC consists of three main components, i.e., Identity Masked Multi-Head Attention (IMMHA), Dialogue-based Gated Recurrent Unit (DiaGRU), and Skip-chain Conditional Random Field (SkipCRF). Compared to previous ERC models, EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels. The proposed model attempts to integrate the advantages of attention- and recurrence-based methods at the feature-extraction level. Specifically, IMMHA is applied to capture identity-based global contextual dependencies, while DiaGRU is utilized to extract speaker- and temporal-aware local contextual information. At the classification level, SkipCRF can explicitly mine complex emotional flows from higher-order neighboring utterances in the conversation. Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark datasets. The ablation studies confirm that our modules can effectively model emotional inertia and contagion.
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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 | DailyDialog | EmotionIC | Macro F1 | 54.19 | #7 of 22 | Archive leaderboard | report |
| Emotion Recognition in Conversation | DailyDialog | EmotionIC | Micro-F1 | 60.13 | #7 of 22 | Archive leaderboard | report |
| Emotion Recognition in Conversation | EmoryNLP | EmotionIC | Micro-F1 | 44.31 | #6 of 28 | Archive leaderboard | report |
| Emotion Recognition in Conversation | EmoryNLP | EmotionIC | Weighted-F1 | 40.25 | #6 of 28 | Archive leaderboard | report |
| Emotion Recognition in Conversation | IEMOCAP | EmotionIC | Accuracy | 69.44 | #21 of 59 | Archive leaderboard | report |
| Emotion Recognition in Conversation | IEMOCAP | EmotionIC | Weighted-F1 | 69.61 | #21 of 59 | Archive leaderboard | report |
| Emotion Recognition in Conversation | MELD | EmotionIC | Micro-F1 | 67.59 | #23 of 68 | Archive leaderboard | report |
| Emotion Recognition in Conversation | MELD | EmotionIC | Weighted-F1 | 66.32 | #23 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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