Papers › COSMIC: COmmonSense knowledge for eMotion Identification in Conversations
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations
Deepanway Ghosal, Navonil Majumder, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria
In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates different elements of commonsense such as mental states, events, and causal relations, and build upon them to learn interactions between interlocutors participating in a conversation. Current state-of-the-art methods often encounter difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes. By learning distinct commonsense representations, COSMIC addresses these challenges and achieves new state-of-the-art results for emotion recognition on four different benchmark conversational datasets. Our code is available at https://github.com/declare-lab/conv-emotion.
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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 | COSMIC | Macro F1 | 51.05 | #12 of 22 | Archive leaderboard | report |
| Emotion Recognition in Conversation | DailyDialog | COSMIC | Micro-F1 | 58.48 | #12 of 22 | Archive leaderboard | report |
| Emotion Recognition in Conversation | EmoryNLP | COSMIC | Weighted-F1 | 38.11 | #18 of 28 | Archive leaderboard | report |
| Emotion Recognition in Conversation | IEMOCAP | COSMIC | Weighted-F1 | 65.30 | #43 of 59 | Archive leaderboard | report |
| Emotion Recognition in Conversation | MELD | COSMIC | Weighted-F1 | 65.21 | #33 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.
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