From Multilingual Complexity to Emotional Clarity: Leveraging
  Commonsense to Unveil Emotions in Code-Mixed Dialogues
Shivani Kumar Ramaneswaran S Md Shad Akhtar Tanmoy Chakraborty
Understanding emotions during conversation is a fundamental aspect of human
communication, driving NLP research for Emotion Recognition in Conversation
(ERC). While considerable research has focused on discerning emotions of
individual speakers in monolingual dialogues, understanding the emotional
dynamics in code-mixed conversations has received relatively less attention.
This motivates our undertaking of ERC for code-mixed conversations in this
study. Recognizing that emotional intelligence encompasses a comprehension of
worldly knowledge, we propose an innovative approach that integrates
commonsense information with dialogue context to facilitate a deeper
understanding of emotions. To achieve this, we devise an efficient pipeline
that extracts relevant commonsense from existing knowledge graphs based on the
code-mixed input. Subsequently, we develop an advanced fusion technique that
seamlessly combines the acquired commonsense information with the dialogue
representation obtained from a dedicated dialogue understanding module. Our
comprehensive experimentation showcases the substantial performance improvement
obtained through the systematic incorporation of commonsense in ERC. Both
quantitative assessments and qualitative analyses further corroborate the
validity of our hypothesis, reaffirming the pivotal role of commonsense
integration in enhancing ERC.