Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
38 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Personality-affected Emotion Generation in Dialog Systems (2404.07229v1)

Published 3 Apr 2024 in cs.CL and cs.AI

Abstract: Generating appropriate emotions for responses is essential for dialog systems to provide human-like interaction in various application scenarios. Most previous dialog systems tried to achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional responses generated by those methods may be inconsistent, which will decrease user engagement and service quality. Psychological findings suggest that the emotional expressions of humans are rooted in personality traits. Therefore, we propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system and further investigate a solution through the personality-affected mood transition. Specifically, we first construct a daily dialog dataset, Personality EmotionLines Dataset (PELD), with emotion and personality annotations. Subsequently, we analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Finally, we propose to model the personality as the transition weight by simulating the mood transition process in the dialog system and solve the challenges above. We conduct extensive experiments on PELD for evaluation. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (81)
  1. Integrating models of personality and emotions into lifelike characters. In International Workshop on Affective Interactions. Springer, 150–165.
  2. Affective neural response generation. In European Conference on Information Retrieval. Springer, 154–166.
  3. G Ball and J Breese. 2000. Emotion and personality ina conversational character. Embodied Conversational Agents. MIT Press, Cambridge (2000).
  4. Emotional states and personality profiles in Conversational AI. (2020).
  5. Sven Buechel and Udo Hahn. 2016. Emotion analysis as a regression problem–dimensional models and their implications on emotion representation and metrical evaluation. In ECAI 2016. IOS Press, 1114–1122.
  6. Sven Buechel and Udo Hahn. 2017. Emobank: Studying the impact of annotation perspective and representation format on dimensional emotion analysis. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. 578–585.
  7. IEMOCAP: Interactive emotional dyadic motion capture database. Language resources and evaluation 42, 4 (2008), 335–359.
  8. The stuff that verbal person-centered support is made of: Identifying linguistic markers of more and less supportive conversations. Journal of Language and Social Psychology 37, 6 (2018), 656–679.
  9. Kenneth Mark Colby. 1975. Artificial paranoia: a computer simulation of paranoid process. Pergamon Press.
  10. Affect-driven dialog generation. arXiv preprint arXiv:1904.02793 (2019).
  11. Paul T Costa and Robert R McCrae. 1992. Normal personality assessment in clinical practice: The NEO Personality Inventory. Psychological assessment 4, 1 (1992), 5.
  12. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
  13. Paul Ed Ekman and Richard J Davidson. 1994. The nature of emotion: Fundamental questions. Oxford University Press.
  14. Howard S Friedman and Margaret L Kern. 2014. Personality, well-being, and health. Annual review of psychology 65 (2014), 719–742.
  15. Dialoguegcn: A graph convolutional neural network for emotion recognition in conversation. arXiv preprint arXiv:1908.11540 (2019).
  16. A design for smooth transition of robotic emotional states. In 2010 IEEE Workshop on Advanced Robotics and its Social Impacts. IEEE, 13–18.
  17. Robotic emotional expression generation based on mood transition and personality model. IEEE transactions on cybernetics 43, 4 (2012), 1290–1303.
  18. Icon: Interactive conversational memory network for multimodal emotion detection. In Proceedings of the 2018 conference on empirical methods in natural language processing. 2594–2604.
  19. Conversational memory network for emotion recognition in dyadic dialogue videos. In Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting, Vol. 2018. NIH Public Access, 2122.
  20. Challenges in building intelligent open-domain dialog systems. ACM Transactions on Information Systems (TOIS) 38, 3 (2020), 1–32.
  21. Mood-transition-based emotion generation model for the robot’s personality. In 2009 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2878–2883.
  22. Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings. arXiv preprint arXiv:1911.09304 (2019).
  23. Michael Johns and Barry G Silverman. 2001. How emotions and personality effect the utility of alternative decisions: a terrorist target selection case study. Center for Human Modeling and Simulation (2001), 10.
  24. SIMPLEX–simulation of personal emotion experience. Affective Computing (2008), 255–270.
  25. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
  26. Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human–robot interaction. Journal of communication 56, 4 (2006), 754–772.
  27. Changmao Li and Jinho D Choi. 2020. Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering. arXiv preprint arXiv:2004.03561 (2020).
  28. A persona-based neural conversation model. arXiv preprint arXiv:1603.06155 (2016).
  29. Reinforcement Learning Based Emotional Editing Constraint Conversation Generation. arXiv preprint arXiv:1904.08061 (2019).
  30. Affective State Prediction of Contextualized Concepts. In IJCAI 2017 Workshop on Artificial Intelligence in Affective Computing. PMLR, 45–57.
  31. Inferring affective meanings of words from word embedding. IEEE Transactions on Affective Computing 8, 4 (2017), 443–456.
  32. Dailydialog: A manually labelled multi-turn dialogue dataset. arXiv preprint arXiv:1710.03957 (2017).
  33. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision. 2980–2988.
  34. Modeling Intra-and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis. In Proceedings of the 29th International Conference on Computational Linguistics. 7124–7135.
  35. MoEL: Mixture of Empathetic Listeners. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 121–132. https://doi.org/10.18653/v1/D19-1012
  36. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).
  37. A survey on empathetic dialogue systems. Information Fusion 64 (2020), 50–70.
  38. François Mairesse and Marilyn A Walker. 2011. Controlling user perceptions of linguistic style: Trainable generation of personality traits. Computational Linguistics 37, 3 (2011), 455–488.
  39. MIME: MIMicking emotions for empathetic response generation. arXiv preprint arXiv:2010.01454 (2020).
  40. Dialoguernn: An attentive rnn for emotion detection in conversations. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 6818–6825.
  41. Robert P Marinier and John E Laird. 2007. Computational modeling of mood and feeling from emotion. In Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 29.
  42. Naoki Masuyama and Chu Kiong Loo. 2015. Robotic emotional model with personality factors based on Pleasant-Arousal scaling model. In 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, 19–24.
  43. Personality affected robotic emotional model with associative memory for human-robot interaction. Neurocomputing 272 (2018), 213–225.
  44. John D Mayer. 2004. (2004) What is Emotional Intelligence? (2004).
  45. Albert Mehrabian. 1996a. Analysis of the big-five personality factors in terms of the PAD temperament model. Australian journal of Psychology 48, 2 (1996), 86–92.
  46. Albert Mehrabian. 1996b. Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology 14, 4 (1996), 261–292.
  47. Saif Mohammad. 2018. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 english words. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 174–184.
  48. Automatic personality assessment through social media language. Journal of personality and social psychology 108, 6 (2015), 934.
  49. Dimensional Emotion Detection from Categorical Emotion. arXiv preprint arXiv:1911.02499 (2019).
  50. Tim Polzehl. 2016. PERSONALITY IN SPEECH. Springer.
  51. Meld: A multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508 (2018).
  52. Emotion recognition in conversation: Research challenges, datasets, and recent advances. IEEE Access 7 (2019), 100943–100953.
  53. Paulo Quaglio. 2009. Television dialogue: The sitcom Friends vs. natural conversation. Amsterdam, NL.
  54. Towards empathetic open-domain conversation models: A new benchmark and dataset. arXiv preprint arXiv:1811.00207 (2018).
  55. James A Russell. 1991. Culture and the categorization of emotions. Psychological bulletin 110, 3 (1991), 426.
  56. James A Russell and Albert Mehrabian. 1977. Evidence for a three-factor theory of emotions. Journal of research in Personality 11, 3 (1977), 273–294.
  57. Personality, gender, and age in the language of social media: The open-vocabulary approach. PloS one 8, 9 (2013), e73791.
  58. Neural dialogue system with emotion embeddings. In 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC). IEEE, 1–4.
  59. A computational approach to understanding empathy expressed in text-based mental health support. arXiv preprint arXiv:2009.08441 (2020).
  60. Dual memory network model for sentiment analysis of review text. Knowledge-Based Systems 188 (2020), 105004.
  61. Supervised prototypical contrastive learning for emotion recognition in conversation. arXiv preprint arXiv:2210.08713 (2022).
  62. Emotional human machine conversation generation based on SeqGAN. In 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia). IEEE, 1–6.
  63. Yla R Tausczik and James W Pennebaker. 2010. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of language and social psychology 29, 1 (2010), 24–54.
  64. Affective instability: measuring a core feature of borderline personality disorder with ecological momentary assessment. Journal of abnormal psychology 117, 3 (2008), 647.
  65. A Wagner and E Briscoe. 2017. Psychological modeling of humans by assistive robots. In Human Modelling for Bio-Inspired Robotics. Elsevier, 273–296.
  66. Transsituational individual-specific biopsychological classification of emotions. IEEE Transactions on Systems, Man, and Cybernetics: Systems 43, 4 (2013), 988–995.
  67. Contextualized emotion recognition in conversation as sequence tagging. In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 186–195.
  68. Jason Wei and Kai Zou. 2019. Eda: Easy data augmentation techniques for boosting performance on text classification tasks. arXiv preprint arXiv:1901.11196 (2019).
  69. Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1401–1410.
  70. Bernard L Welch. 1947. The generalization of STUDENT’S problem when several different population varlances are involved. Biometrika 34, 1-2 (1947), 28–35.
  71. Automatically Select Emotion for Response via Personality-affected Emotion Transition. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 5010–5020.
  72. Decode with template: Content preserving sentiment transfer. In Proceedings of the Twelfth Language Resources and Evaluation Conference. 4671–4679.
  73. DesPrompt: Personality-descriptive prompt tuning for few-shot personality recognition. Information Processing & Management 60, 5 (2023), 103422.
  74. Tal Yarkoni. 2010. Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. Journal of research in personality 44, 3 (2010), 363–373.
  75. Sayyed M Zahiri and Jinho D Choi. 2017. Emotion detection on tv show transcripts with sequence-based convolutional neural networks. arXiv preprint arXiv:1708.04299 (2017).
  76. Rohola Zandie and Mohammad H Mahoor. 2020. Emptransfo: A multi-head transformer architecture for creating empathetic dialog systems. arXiv preprint arXiv:2003.02958 (2020).
  77. Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. arXiv preprint arXiv:1703.10960 (2017).
  78. Comae: a multi-factor hierarchical framework for empathetic response generation. arXiv preprint arXiv:2105.08316 (2021).
  79. Towards persona-based empathetic conversational models. arXiv preprint arXiv:2004.12316 (2020).
  80. Emotional chatting machine: Emotional conversation generation with internal and external memory. In Thirty-Second AAAI Conference on Artificial Intelligence.
  81. The design and implementation of xiaoice, an empathetic social chatbot. Computational Linguistics 46, 1 (2020), 53–93.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Zhiyuan Wen (11 papers)
  2. Jiannong Cao (73 papers)
  3. Jiaxing Shen (14 papers)
  4. Ruosong Yang (8 papers)
  5. Shuaiqi Liu (12 papers)
  6. Maosong Sun (337 papers)
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets