COSMIC: COmmonSense knowledge for eMotion Identification in Conversations

Deepanway Ghosal, Navonil Majumder, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria


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.
Anthology ID:
2020.findings-emnlp.224
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2470–2481
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.224
DOI:
10.18653/v1/2020.findings-emnlp.224
Bibkey:
Cite (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.
Cite (Informal):
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (Ghosal et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.224.pdf
Code
 declare-lab/conv-emotion
Data
DailyDialogEmoryNLPIEMOCAPMELD