Elamur Research⌕
☰
home / papers / 2026 / Who You Are, What You Say: Intra- and Inter- Context Personality for Emotion Recognition in Conversation

Who You Are, What You Say: Intra- and Inter- Context Personality for Emotion Recognition in Conversation

Paper · 2026 · Tazeek Bin Abdur Rakib, Lay-Ki Soon and Wern Han Lim

Summary

Emotion recognition in conversation (ERC) depends on both the dialogue context and who is speaking. Yet most systems merge all prior utterances into one context vector and represent speakers only superficially. The authors introduce PERC, a framework that brings personality into ERC. It first splits the history of a target utterance into two streams: the queried speaker's own turns (intra) and everyone else's turns (inter). For each stream it extracts a context representation and a personality representation, the latter from a pre-trained personality extractor grounded in the Big Five. Personality can be computed once (static) or updated over a sliding window of turns (dynamic). A contrastive cross-alignment objective then pairs intra-personality with intra-context and inter-personality with inter-context, and an MLP predicts the emotion. Unlike ERC-DP, PERC uses no future utterances, which keeps it suitable for real-time use. On MELD, EmoryNLP and IEMOCAP it sets new state-of-the-art weighted F1. This suggests that explicit, context-sensitive personality cues help emotion reasoning without external commonsense knowledge bases.

Key points

  • Weighted F1 results: 70.25 on MELD, 42.11 on EmoryNLP and 73.14 on IEMOCAP.
    • Gains over the best non-LLM baselines are +2.74, +1.17 and +1.44.
    • The margin over LLM-based systems such as LaERC-S, InstructERC and BiosERC is up to 0.98.
  • Dynamic vs. static personality: dynamic personality clearly beats static.
    • With a window of n=4, the model reaches 70.25 on MELD and 73.14 on IEMOCAP.
    • Without personality, it scores 67.41 and 68.32.
    • Static personality is best at n=2 and degrades as the window grows.
  • Context segregation: jointly modeling separate intra and inter contexts outperforms using the full context (+0.82 on MELD, +2.59 on IEMOCAP).
    • Using only intra-context hurts most (–3.54 on MELD).
  • Cross-alignment: the contrastive alignment adds about +1.1 F1 on both MELD and IEMOCAP.
  • Stated limitations:
    • Treating inter pairs as negatives ignores empathy or mimicry between speakers.
    • Recomputing dynamic personality at every turn adds computational cost.
    • Concatenation-based pooling of speakers loses per-speaker detail.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Monash University MalaysiaSubang Jaya · Malaysia4 works · 10 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksPsychology · 1,649 worksExperimental and Cognitive Psychology · 930 works

🧭 Topics 1Research subjects and languages the work is about.Sentiment & emotion · 1,035 works

Cite

Cite as Tazeek Bin Abdur Rakib, Lay-Ki Soon and Wern Han Lim. 2026. Who You Are, What You Say: Intra- and Inter- Context Personality for Emotion Recognition in Conversation. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-eacl.172

@inproceedings{rakib2026who,
  title = {{Who You Are, What You Say: Intra- and Inter- Context Personality for Emotion Recognition in Conversation}},
  author = {Tazeek {Bin Abdur Rakib}, Lay-Ki Soon and Wern Han Lim},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.172},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.172}
}

References

69 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Tim Althoff, Kevin Clark, and Jure Leskovec. 2016. Large-scale analysis of counseling conversations: An application of natural language processing to mental health. Transactions of the Association for Computational Linguistics, 4:463–476.
  2. Yinan Bao, Qianwen Ma, Lingwei Wei, Wei Zhou, and Songlin Hu. 2022. Speaker-guided encoder-decoder framework for emotion recognition in conversation. arXiv preprint arXiv:2206.03173. — [arXiv:2206.03173](https://arxiv.org/abs/2206.03173) find
  3. Urszula Barańczuk. 2019. The five factor model of personality and emotion regulation: A meta-analysis. Personality and individual differences, 139:217–227. find
  4. Anna Baumert, Manfred Schmitt, Marco Perugini, Wendy Johnson, Gabriela Blum, Peter Borkenau, Giulio Costantini, Jaap JA Denissen, William Fleeson, Ben Grafton, and 1 others. 2017. Integrating personality structure, personality process, and personality development. European Journal of Personality, 31… find
  5. Nadin Beckmann and Robert E Wood. 2017. Dynamic personality science. integrating between-person stability and within-person change. find
  6. Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019. Comet: Commonsense transformers for automatic knowledge graph construction. arXiv preprint arXiv:1906.05317.
  7. Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N Chang, Sungbok Lee, and Shrikanth S Narayanan. 2008. Iemocap: Interactive emotional dyadic motion capture database. Language resources and evaluation, 42(4):335–359. find
  8. Paul T Costa Jr and Robert R McCrae. 1992. Four ways five factors are basic. Personality and individual differences, 13(6):653–665. find
  9. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  10. Paul Ekman. 1971. Universals and cultural differences in facial expressions of emotion. In Nebraska symposium on motivation. University of Nebraska Press. find
  11. Paul Ekman. 1992. Are there basic emotions? find
  12. William Fleeson. 2007. Situation-based contingencies underlying trait-content manifestation in behavior. Journal of personality, 75(4):825–862. find
  13. Yumeng Fu, Junjie Wu, Zhongjie Wang, Meishan Zhang, Lili Shan, Yulin Wu, and Bingquan Liu. 2025. Laerc-s: Improving llm-based emotion recognition in conversation with speaker characteristics. In Proceedings of the 31st International Conference on Computational Linguistics, pages 6748–6761. find
  14. Adrian Furnham. 1996. The big five versus the big four: the relationship between the myers-briggs type indicator (mbti) and neo-pi five factor model of personality. Personality and individual differences, 21(2):303– 307. find
  15. Qingqing Gao, Jiuxin Cao, Biwei Cao, Xin Guan, and Bo Liu. 2024. Cept: a contrast-enhanced prompt-tuning framework for emotion recognition in conversation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pa… find
  16. Deepanway Ghosal, Navonil Majumder, Alexander Gelbukh, Rada Mihalcea, and Soujanya Poria. 2020. Cosmic: Commonsense knowledge for emotion identification in conversations. arXiv preprint arXiv:2010.02795. find
  17. Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, and Alexander Gelbukh. 2019. Dialoguegcn: A graph convolutional neural network for emotion recognition in conversation. arXiv preprint arXiv:1908.11540.
  18. Samuel D Gosling, Peter J Rentfrow, and William B Swann Jr. 2003. A very brief measure of the big-five personality domains. Journal of Research in personality, 37(6):504–528. find
  19. James J Gross. 1998. The emerging field of emotion regulation: An integrative review. Review of general psychology, 2(3):271–299. find
  20. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735– 1780. find
  21. Dou Hu, Yinan Bao, Lingwei Wei, Wei Zhou, and Songlin Hu. 2023. Supervised adversarial contrastive learning for emotion recognition in conversations. arXiv preprint arXiv:2306.01505. find
  22. Dou Hu, Lingwei Wei, and Xiaoyong Huai. 2021. Dialoguecrn: Contextual reasoning networks for emotion recognition in conversations. arXiv preprint arXiv:2106.01978. find
  23. Taichi Ishiwatari, Yuki Yasuda, Taro Miyazaki, and Jun Goto. 2020. Relation-aware graph attention networks with relational position encodings for emotion recognition in conversations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7360–7370. find
  24. Wenxiang Jiao, Michael Lyu, and Irwin King. 2020. Real-time emotion recognition via attention gated hierarchical memory network. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 8002–8009. find
  25. Wenxiang Jiao, Haiqin Yang, Irwin King, and Michael R. Lyu. 2019. HiGRU: Hierarchical gated recurrent units for utterance-level emotion recognition. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volu… find
  26. Carl Gustav Jung, Gerhard Adler, and Richard Francis Carrington Hull. 2014. The development of personality. Routledge. find
  27. Yujin Kang and Yoon-Sik Cho. 2024. Improving contrastive learning in emotion recognition in conversation via data augmentation and decoupled neutral emotion. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages… find
  28. Yujin Kang and Yoon-Sik Cho. 2025. Beyond single emotion: Multi-label approach to conversational emotion recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 24321–24329. find
  29. Taewoon Kim and Piek Vossen. 2021. Emoberta: Speaker-aware emotion recognition in conversation with roberta. arXiv preprint arXiv:2108.12009. — [arXiv:2108.12009](https://arxiv.org/abs/2108.12009) find
  30. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  31. Randy J Larsen and Timothy Ketelaar. 1991. Personality and susceptibility to positive and negative emotional states. Journal of personality and social psychology, 61(1):132. find
  32. Joosung Lee and Wooin Lee. 2021. Compm: Context modeling with speaker’s pre-trained memory tracking for emotion recognition in conversation. arXiv preprint arXiv:2108.11626. find
  33. Shanglin Lei, Guanting Dong, Xiaoping Wang, Keheng Wang, Runqi Qiao, and Sirui Wang. 2023. Instructerc: Reforming emotion recognition in conversation with multi-task retrieval-augmented large language models. arXiv preprint arXiv:2309.11911. — [arXiv:2309.11911](https://arxiv.org/abs/2309.11911) find
  34. Sointu Leikas, Jan-Erik Lönnqvist, and Markku Verkasalo. 2012. Persons, situations, and behaviors: Consistency and variability of different behaviors in four interpersonal situations. Journal of Personality and Social Psychology, 103(6):1007. find
  35. Michael Lewis. 1999. On the development of personality. Handbook of personality: Theory and research. find
  36. Jiang Li, Xiaoping Wang, Yingjian Liu, and Zhigang Zeng. 2024a. Ernetcl: A novel emotion recognition network in textual conversation based on curriculum learning strategy. Knowledge-based Systems, 286:111434. find
  37. Jiang Li, Xiaoping Wang, and Zhigang Zeng. 2024b. A dual-stream recurrence-attention network with global–local awareness for emotion recognition in textual dialog. Engineering Applications of Artificial Intelligence, 128:107530. find
  38. Jiangnan Li, Zheng Lin, Peng Fu, and Weiping Wang. 2021. Past, present, and future: Conversational emotion recognition through structural modeling of psychological knowledge. In Findings of the association for computational linguistics: EMNLP 2021, pages 1204–1214. find
  39. Jingye Li, Donghong Ji, Fei Li, Meishan Zhang, and Yijiang Liu. 2020. Hitrans: A transformer-based context-and speaker-sensitive model for emotion detection in conversations. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4190–4200. find
  40. Shimin Li, Hang Yan, and Xipeng Qiu. 2022. Contrast and generation make bart a good dialogue emotion recognizer. In Proceedings of the AAAI conference on artificial intelligence, volume 36, pages 11002– 11010. find
  41. James W Pennebaker and Laura A King. 1999. Linguistic styles: language use as an individual difference. Journal of personality and social psychology, 77(6):1296. find
  42. Verónica Pérez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence An, Kathy J Goggin, and Delwyn Catley. 2017. Predicting counselor behaviors in motivational interviewing encounters. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Ling… find
  43. Ximing Li, Yuanchao Dai, Zhiyao Yang, Jinjin Chi, Wanfu Gao, and Lin Yuanbo Wu. 2025. Utterance-level emotion recognition in conversation with conversation-level supervision. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 24503–24511. find
  44. Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea. 2018. Meld: A multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508.
  45. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980–2988.
  46. Xiangyu Qin, Zhiyu Wu, Tingting Zhang, Yanran Li, Jian Luan, Bin Wang, Li Wang, and Jinshi Cui. 2023. Bert-erc: Fine-tuning bert is enough for emotion recognition in conversation. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 13492–13500. find
  47. Zhaojiang Lin, Andrea Madotto, Jamin Shin, Peng Xu, and Pascale Fung. 2019. Moel: Mixture of empathetic listeners. arXiv preprint arXiv:1908.07687.
  48. Yingjian Liu, Jiang Li, Xiaoping Wang, and Zhigang Zeng. 2024. Emotionic: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation. Science China Information Sciences, 67(8):182103. find
  49. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
  50. Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, and Soujanya Poria. 2020. Mime: Mimicking emotions for empathetic response generation. arXiv preprint arXiv:2010.01454.
  51. Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, and Erik Cambria. 2019. Dialoguernn: An attentive rnn for emotion detection in conversations. In Proceedings of the AAAI conference on artificial intelligence, pages 6818–6825. find
  52. Louis-Philippe Morency. 2013. The role of context in affective behavior understanding. Social Emotions in Nature and Artifact, 2:8–27. find
  53. Julian A Nasello, Jean-Marc Triffaux, and Michel Hansenne. 2023. Individual differences and personality traits across situations. Current Issues in Personality Psychology, 12(2):109. find
  54. Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748.
  55. Tazeek Bin Abdur Rakib, Ambuj Mehrish, Lay-Ki Soon, Wern Han Lim, and Soujanya Poria. 2025. Dialogxpert: Driving intelligent and emotion-aware conversations through online value-based reinforcement learning with llm priors. arXiv preprint arXiv:2505.17795. — [arXiv:2505.17795](https://arxiv.org/abs/2505.17795) find
  56. Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2018. Towards empathetic open-domain conversation models: A new benchmark and dataset. arXiv preprint arXiv:1811.00207. find
  57. Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, and Yejin Choi. 2019. Atomic: An atlas of machine commonsense for if-then reasoning. Preprint, arXiv:1811.00146. find
  58. Weizhou Shen, Junqing Chen, Xiaojun Quan, and Zhixian Xie. 2021a. Dialogxl: All-in-one xlnet for multi-party conversation emotion recognition. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 13789–13797. find
  59. Weizhou Shen, Siyue Wu, Yunyi Yang, and Xiaojun Quan. 2021b. Directed acyclic graph network for conversational emotion recognition. arXiv preprint arXiv:2105.12907.
  60. Xiaohui Song, Longtao Huang, Hui Xue, and Songlin Hu. 2022. Supervised prototypical contrastive learning for emotion recognition in conversation. arXiv preprint arXiv:2210.08713. find
  61. Yan Wang, Bo Wang, Yachao Zhao, Dongming Zhao, Xiaojia Jin, Jijun Zhang, Ruifang He, and Yuexian Hou. 2024. Emotion recognition in conversation via dynamic personality. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COL… find
  62. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1):4–24.
  63. Camille J Wynn, Tyson S Barrett, and Stephanie A Borrie. 2024. Conversational speech behaviors are context dependent. Journal of Speech, Language, and Hearing Research, 67(5):1360–1369. find
  64. Jieying Xue, Minh-Phuong Nguyen, Blake Matheny, and Le-Minh Nguyen. 2024. Bioserc: Integrating biography speakers supported by llms for erc tasks. In International Conference on Artificial Neural Networks, pages 277–292. Springer. find
  65. Lin Yang, Yi Shen, Yue Mao, and Longjun Cai. 2022. Hybrid curriculum learning for emotion recognition in conversation. In Proceedings of the AAAI conference on artificial intelligence, volume 36, pages 11595–11603. find
  66. Zhilin Yang. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237.
  67. Sayyed M Zahiri and Jinho D Choi. 2018. Emotion detection on tv show transcripts with sequence-based convolutional neural networks. In AAAI Workshops, volume 18, pages 44–52. find
  68. Weixiang Zhao, Yanyan Zhao, and Xin Lu. 2022. Cauain: Causal aware interaction network for emotion recognition in conversations. In IJCAI, pages 4524–4530. find
  69. Lixing Zhu, Gabriele Pergola, Lin Gui, Deyu Zhou, and Yulan He. 2021. Topic-driven and knowledge-aware transformer for dialogue emotion detection. arXiv preprint arXiv:2106.01071. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/rakib-2026-who-you-are-what-you/