Emotionally intelligent chatbots-designing for empathy and emotional support: A review
Abstract
The integration of emotional Intelligence into chatbots for empathy and support has brought dramatic development to many sectors. The development has led researchers to continue experimenting and studying diverse design techniques (novel and existing) to find the method(s) that best suit the building of emotionally intelligent chatbots. To address this challenge, this paper provides a reviewed literature that extracted features such as the work done, findings, limitations, and the design techniques adopted to either improve an existing model or design a new model. Findings from the reviewed papers indicate that the reviewed paper mostly used the sequence-to-sequence (seq2seq) model framework while incorporating it with other design techniques to improve on the seq2seq model. This is done based on the domain the model is built. However, other design techniques adopted by other reviewed papers are conditional variational auto-encoder, transformers, and a host of others. Although the researchers also incorporated other models with their main design technique to have a better system model.
Downloads
References
E. Svikhnushina, & P. Pu, “Social and emotional etiquette of chatbots: A qualitative approach to understanding user needs and expectations,” arXiv preprint, 2020, arXiv:2006.13883
Y. C. Chang, & Y. C. Hsing, “Emotion-infused deep neural network for emotionally resonant conversation,” Applied Soft Computing, vol. 113, p. 107861, 2021.
M. Skjuve, A. Følstad, K. I. Fostervold, & P. B. Brandtzaeg, “My chatbot companion-a study of human-chatbot relationships,” Int’l Journal of Human-Computer Studies, vol. 149, p. 102601, 2021.
M. Adam, M. Wessel, & A. Benlian, “AI-based chatbots in customer service and their effects on user compliance,” Electronic Markets, vol. 31, no. 2, pp. 427-445, 2021.
J. S. Chen, T. T. Y. Le, & D. Florence, “Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing,” Int’l Journal of Retail & Distribution Management, vol. 49, no. 11, pp. 1512-1531, 2021.
J. Hu, Y. Huang, X. Hu, & Y. Xu, “Enhancing the perceived emotional intelligence of conversational agents through acoustic cues,” In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1-7, 2021.
E. Adamopoulou, & L. Moussiades, “An overview of chatbot technology. In Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 Int’l Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part II 16 (pp. 373-383). Springer International Publishing, 2020.
E. Adamopoulou, & L. Moussiades, “Chatbots: History, technology, and applications,” Machine Learning with Applications, vol. 2, p. 100006, 2020.
H. Y. Shum, X. D. He, & D. Li, “From Eliza to XiaoIce: challenges and opportunities with social chatbots,” Frontiers of Information Technology & Electronic Engineering, vol. 19, pp. 10-26, 2018.
K. A. L. R. Carranza, J. Manalili, N. T. Bugtai, & R. G. Baldovino, “Expression tracking with OpenCV deep learning for a development of emotionally aware chatbots,” In 2019 7th IEEE Int’l Conference on Robot Intelligence Technology and Applications (RiTA), pp. 160-163, 2019.
G. Bilquise, S. Ibrahim, & K Shaalan, “Emotionally intelligent chatbots: A systematic literature review,” Human Behavior and Emerging Technologies, vol. 2022, pp. 23, 2022.
S. Hussain, O. Ameri Sianaki, & N. Ababneh, “A survey on conversational agents/chatbots classification and design techniques,” In Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 33rd Int’l Conference on Advanced Information Networking and Applications (WAINA-2019), vol. 33, pp. 946-956, 2019, Springer Int’l Publishing.
P. Suta, X. Lan, B. Wu, P. Mongkolnam, & J. H. Chan, “An overview of machine learning in chatbots,” Int’l Journal of Mechanical Engineering and Robotics Research, vol. 9, no. 4, pp. 502-510, 2020.
A. Ghandeharioun, D. McDuff, M. Czerwinski, & K. Rowan, “Emma: An emotion-aware wellbeing chatbot,” In 2019 8th IEEE Int’l Conference on Affective Computing and Intelligent Interaction (ACII), pp. 1-7, 2019.
J. L. Beredo, & E. C. Ong, “A hybrid response generation model for an empathetic conversational agent,” In 2022 IEEE Int’l Conference on Asian Language Processing (IALP), pp. 300-305, 2022.
R. Goel, S. Vashisht, A. Dhanda, & S. Susan, “An empathetic conversational agent with attentional mechanism. In 2021 IEEE Int’l Conference on Computer Communication and Informatics (ICCCI), pp. 1-4, 2021.
S. S. Abdullahi, S. Yiming, A. Abdullahi, & U. Aliyu, “Open domain chatbot based on attentive end-to-end Seq2Seq mechanism,” In Proceedings of the 2019 2nd Int’l Conference on Algorithms, Computing and Artificial Intelligence, pp. 339-344, 2019.
R. K. Gupta, & Y. Yang, “Crystalfeel at semeval-2018 task 1: Understanding and detecting emotion intensity using affective lexicons,” In Proceedings of The 12th Int’l Workshop on Semantic Evaluation, pp. 256-263, 2018.
H. Rashkin, E. M. Smith, M. Li, & Y. L. Boureau, “Towards empathetic open-domain conversation models: A new benchmark and dataset. arXiv preprint, 2018, arXiv:1811.00207.
N. Asghar, P. Poupart, J. Hoey, X. Jiang, & L. Mou, “Affective neural response generation,” In Advances in Information Retrieval: 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018, Proceedings 40, pp. 154-166, Springer Int’l Publishing.
P. Huo, Y. Yang, J. Zhou, C. Chen, & L. He, “Terg: Topic-aware emotional response generation for chatbot,” In 2020 IEEE Int’l Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2020.
S. Li, S. Feng, D. Wang, K. Song, Y. Zhang, & W. Wang, “EmoElicitor: an open domain response generation model with user emotional reaction awareness. In Proceedings of the Twenty-Ninth Int’l Conference on International Joint Conferences on Artificial Intelligence, pp. 3637-3643, 2021.
D. Peng, M. Zhou, C. Liu, & J. Ai, “Human–machine dialogue modeling with the fusion of word-and sentence-level emotions,” Knowledge-Based Systems, vol. 192, p. 105319, 2020.
K. Yao, L. Zhang, T. Luo, D. Du, & Y. Wu, “Non-deterministic and emotional chatting machine: learning emotional conversation generation using conditional variational autoencoders,” Neural Computing and Applications, vol. 33, pp. 5581-5589, 2021.
R. Zhang, J. Guo, Y. Fan, Y. Lan, & X. Cheng, “Dual-factor generation model for conversation,” ACM Transactions on Information Systems (TOIS), vol. 38, no. 3, pp. 1-31, 2020.
Y. Peng, Y. Fang, Z. Xie, & G. Zhou, “Topic-enhanced emotional conversation generation with attention mechanism,” Knowledge-Based Systems, vol. 163, pp. 429-437, 2019.
W. Wei, J. Liu, X. Mao, G. Guo, F. Zhu, P. Zhou, ... & S. Feng, “Target-guided emotion-aware chat machine,” ACM Transactions on Information Systems (TOIS), vol. 39, no. 4, pp. 1-24, 2021.
P. Zhong, D. Wang, & C. Miao, “An affect-rich neural conversational model with biased attention and weighted cross-entropy loss,” In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 7492-7500, 2019.
Q. Li, H. Chen, Z. Ren, P. Ren, Z. Tu, & Z. Chen, “EmpDG: Multiresolution interactive empathetic dialogue generation,” arXiv preprint, 2019, arXiv:1911.08698.
Z. Lin, A. Madotto, J. Shin, P. Xu, & P. Fung, “Moel: Mixture of empathetic listeners,” arXiv preprint, 2019, arXiv:1908.07687
S. Mohammad, “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, vol. 1: Long papers, pp. 174-184, 2018.
R. K. Gupta, & Y. Yang, “Crystalfeel at semeval-2018 task 1: Understanding and detecting emotion intensity using affective lexicons,” In Proceedings of The 12th Int’l Workshop on Semantic Evaluation, pp. 256-263, 2018.
Z. Xiao, M. X. Zhou, W. Chen, H. Yang, & C. Chi, “If I hear you correctly: Building and evaluating interview chatbots with active listening skills,” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-14, 2020.
N. Majumder, P. Hong, S. Peng, J. Lu, D. Ghosal, A. Gelbukh, & S. Poria, “MIME: MIMicking emotions for empathetic response generation,” arXiv preprint, 2020, arXiv:2010.01454
Copyright (c) 2023 Sumayya Abubakar, Idris Rabiu, Amit Mishra, Ismaila Musa
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
