Who You Are, What You Say: Intra- and Inter- Context Personality for Emotion Recognition in Conversation
Year: 2026
Type: conference-paper
Abstract: Emotion recognition in conversation (ERC) requires understanding both contextual dependencies and speaker-specific cues.Existing approaches often treat conversation context as a single representation or encode speaker identity shallowly, limiting their ability to capture fine-grained emotional dynamics.We propose PERC, a personality-aware ERC framework that (1) segregates conversational context into intra-and inter-speaker components, (2) models static or dynamic personality traits to represent stable and evolving speaker dispositions, and (3) performs contrastive cross-alignment between intra-intra and inter-inter representations to enforce contextual and personality consistency.Experiments on three ERC benchmarks show that PERC achieves new state-ofthe-art performance, improving weighted F1 by up to 2.74% over non-LLM methods and 0.98% over recent LLM-based methods.Our results demonstrate the effectiveness of integrating context segregation, personality modeling, and contrastive alignment for emotion reasoning in dialogue.
Institution: Monash University Malaysia
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7140109597