%0 Conference Proceedings %T COSMIC: COmmonSense knowledge for eMotion Identification in Conversations %A Ghosal, Deepanway %A Majumder, Navonil %A Gelbukh, Alexander %A Mihalcea, Rada %A Poria, Soujanya %Y Cohn, Trevor %Y He, Yulan %Y Liu, Yang %S Findings of the Association for Computational Linguistics: EMNLP 2020 %D 2020 %8 November %I Association for Computational Linguistics %C Online %F ghosal-etal-2020-cosmic %X 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-theart 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. %R 10.18653/v1/2020.findings-emnlp.224 %U https://aclanthology.org/2020.findings-emnlp.224 %U https://doi.org/10.18653/v1/2020.findings-emnlp.224 %P 2470-2481