@inproceedings{abdur-rakib-etal-2026-say,
    title = "Who You Are, What You Say: Intra- and Inter- Context Personality for Emotion Recognition in Conversation",
    author = "Abdur Rakib, Tazeek Bin  and
      Soon, Lay-Ki  and
      Lim, Wern Han",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.172/",
    pages = "3293--3308",
    ISBN = "979-8-89176-386-9",
    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-of-the-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."
}