COSMIC: COmmonSense knowledge for eMotion Identification in Conversations

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.

PDF Abstract Findings of 2020 PDF Findings of 2020 Abstract
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Emotion Recognition in Conversation DailyDialog COSMIC Macro F1 51.05 # 6
Micro-F1 58.48 # 11
Emotion Recognition in Conversation EmoryNLP COSMIC Weighted-F1 38.11 # 14
Emotion Recognition in Conversation IEMOCAP COSMIC Weighted-F1 65.30 # 30
Emotion Recognition in Conversation MELD COSMIC Weighted-F1 65.21 # 22

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