Research on Emotionally Intelligent Dialogue Generation based on Automatic Dialogue System

Authors

  • Jin Wang University of the People
  • JinFei Wang Sehan University
  • Shuying Dai Indian Institute of Technology Guwahati
  • Jiqiang Yu Universidad Internacional Isabel I de Castilla
  • Keqin Li AMA university

DOI:

https://doi.org/10.5281/zenodo.10864022

References:

6

Keywords:

Natural Language Processing (NLP) Technology, Emotional Analysis Techniques, DMS, Generative Model, Emotional Expression Technology, Machine Learning And Deep Learning Techniques, User Modeling And Emotion Tracking, Pain Empathy In AI

Abstract

Automated dialogue systems are important applications of artificial intelligence, and traditional systems struggle to understand user emotions and provide empathetic feedback. This study integrates emotional intelligence technology into automated dialogue systems and creates a dialogue generation model with emotional intelligence through deep learning and natural language processing techniques. The model can detect and understand a wide range of emotions and specific pain signals in real time, enabling the system to provide empathetic interaction. By integrating the results of the study "Can artificial intelligence detect pain and express pain empathy?", the model's ability to understand the subtle elements of pain empathy has been enhanced, setting higher standards for emotional intelligence dialogue systems. The project aims to provide theoretical understanding and practical suggestions to integrate advanced emotional intelligence capabilities into dialogue systems, thereby improving user experience and interaction quality.

Author Biographies

Jin Wang, University of the People

Currently studying nursing at a university in China, studying for an undergraduate degree in computer science at a university in the United States, and studying for a master's degree in big data and artificial intelligence at a university in Spain. I am passionate about the development of computer science and artificial intelligence.

JinFei Wang, Sehan University

Engaged in acquiring a physiotherapy degree in South Korea and have accumulated multiple years of clinical experience in the field. Concurrently, I am advancing my education in health sciences in the United States. My dedication and enthusiasm are rooted in enhancing physiotherapy methodologies and deepening my understanding of health sciences.

Shuying Dai, Indian Institute of Technology Guwahati

Graduated from a college in China with associate degree, now studying at Indian Institute of Technology Guwahati for Bachelor of Science in Data Science and Artificial Intelligence.

Jiqiang Yu, Universidad Internacional Isabel I de Castilla

Universidad Internacional Isabel I de Castilla, Burgos, Spain.

Keqin Li, AMA university

AMA university bachelor of science in computer science.

References

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Huang, C., Zaiane, O. R., Trabelsi, A., & Dziri, N. (2018, June). Automatic dialogue generation with expressed emotions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), 49-54.

Zang, H. (2024). Precision calibration of industrial 3D scanners: An AI-enhanced approach for improved measurement accuracy. Global Academic Frontiers, 2(1), 27-37.

Ni, F., Zang, H., & Qiao, Y. (2024, January). Smartfix: Leveraging machine learning for proactive equipment maintenance in industry 4.0. In The 2nd International scientific and practical conference “Innovations in education: Prospects and challenges of today” (January 16-19, 2024). Sofia, Bulgaria. International Science Group. 2024. 389 p. (p. 313).

Zang, H., Li, S., Dong, X., Ma, D., & Dang, B. (2024). Evaluating the social impact of AI in manufacturing: A methodological framework for ethical production. Academic Journal of Sociology and Management, 2(1), 21–25. https://doi.org/10.5281/zenodo.10474511

Ma, D., Dang, B., Li, S., Zang, H., & Dong, X. (2023). Implementation of computer vision technology based on artificial intelligence for medical image analysis. International Journal of Computer Science and Information Technology, 1(1), 69-76. https://doi.org/10.62051/ijcsit.v1n1.10

	Research on Emotionally Intelligent Dialogue Generation based on Automatic Dialogue System

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Published

2024-04-27

How to Cite

Wang, J., Wang, J., Dai, S., Yu, J., & Li, K. (2024). Research on Emotionally Intelligent Dialogue Generation based on Automatic Dialogue System. Journal of Computer Technology and Applied Mathematics, 1(1), 1–5. https://doi.org/10.5281/zenodo.10864022

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Articles