  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.
