@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Who You Are, What You Say: Intra- and Inter- Context Personality for Emotion Recognition in Conversation
%A Abdur Rakib, Tazeek Bin
%A Soon, Lay-Ki
%A Lim, Wern Han
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F abdur-rakib-etal-2026-say
%X 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.
%U https://aclanthology.org/2026.findings-eacl.172/
%P 3293-3308
Markdown (Informal)
[Who You Are, What You Say: Intra- and Inter- Context Personality for Emotion Recognition in Conversation](https://aclanthology.org/2026.findings-eacl.172/) (Abdur Rakib et al., Findings 2026)
ACL