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        <title>COSMIC: COmmonSense knowledge for eMotion Identification in Conversations</title>
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        <namePart type="given">Deepanway</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Soujanya</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.224</identifier>
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%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

Markdown (Informal)

[COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://aclanthology.org/2020.findings-emnlp.224) (Ghosal et al., Findings 2020)

• COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (Ghosal et al., Findings 2020)

ACL

• Deepanway Ghosal, Navonil Majumder, Alexander Gelbukh, Rada Mihalcea, and Soujanya Poria. 2020. COSMIC: COmmonSense knowledge for eMotion Identification in Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2470–2481, Online. Association for Computational Linguistics.