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Understanding emotions during conversation is a fundamental aspect of humancommunication, driving NLP research for Emotion Recognition in Conversation(ERC). While considerable research has focused on discerning emotions ofindividual speakers in monolingual dialogues, understanding the emotionaldynamics in code-mixed conversations has received relatively less attention.This motivates our undertaking of ERC for code-mixed conversations in thisstudy. Recognizing that emotional intelligence encompasses a comprehension ofworldly knowledge, we propose an innovative approach that integratescommonsense information with dialogue context to facilitate a deeperunderstanding of emotions. To achieve this, we devise an efficient pipelinethat extracts relevant commonsense from existing knowledge graphs based on thecode-mixed input. Subsequently, we develop an advanced fusion technique thatseamlessly combines the acquired commonsense information with the dialoguerepresentation obtained from a dedicated dialogue understanding module. Ourcomprehensive experimentation showcases the substantial performance improvementobtained through the systematic incorporation of commonsense in ERC. Bothquantitative assessments and qualitative analyses further corroborate thevalidity of our hypothesis, reaffirming the pivotal role of commonsenseintegration in enhancing ERC.

From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues