TNS Vol.2 (CIAP 2022), 02 February 2023
* Author to whom correspondence should be addressed.
Intelligent dialogue systems, as a subfield of artificial intelligence, have very important research significance and application value. Today’s AI dialogue systems are still in a relatively early stage, but they are developing very rapidly. In recent years, intelligent dialogue systems have been applied in many fields, such as intelligent customer service in online transactions, intelligent voice assistants in smartphones, and virtual chatbots. This paper introduces the background of intelligent dialogue systems and the current research status of key technologies and discusses some challenges in this field and some recent research to improve the system. Most of the current intelligent dialogue systems can perform effective human-computer interaction and respond accordingly. But for the next generation of intelligent dialogue system, more human characteristics are needed so that it can better understand and express human language, have its own personality, and maintain the consistency and logic of dialogue.
Natural language processing, Artificial intelligence, Intelligent dialogue system, Chatbot
1. Nicola Bleu. (2021) 29 Top Chatbot Statistics For 2022: Usage, Demographics, Trends. Blogging Wizard. Retrieved June 30, 2022 from https://bloggingwizard.com/chatbot-statistics/
2. Ciarán Daly. (2018) KLM: Chatbots Are The Future Of Customer Support. AI Business. Retrieved June 30, 2022 from https://aibusiness.com/document.asp?doc_id=760517
3. Turing, A. M. (2012) Computing machinery and intelligence (1950). The Essential Turing: the Ideas That Gave Birth to the Computer Age, 433-464.
4. WEIZENBAUM J. (1983) ELIZA-a computer program for the study of natural language communication between man and machine[J]. Communications of the ACM，26(1): 23-28.
5. Bayan AbuShawar and Eric Atwell. (2015) ALICE Chatbot: Trials and Outputs. Computación y Sistemas 19, 4. DOI:https://doi.org/10.13053/cys-19-4-2326
6. Cahn, J. (2017) CHATBOT: Architecture, design, & development. University of Pennsylvania School of Engineering and Applied Science Department of Computer and Information Science.
7. Shum, H. Y., He, X. D., & Li, D. (2018) From Eliza to XiaoIce: challenges and opportunities with social chatbots. Frontiers of Information Technology & Electronic Engineering, 19(1), 10-26.
8. Karpagavalli, S., & Chandra, E. (2016) A review on automatic speech recognition architecture and approaches. International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(4), 393-404.
9. Prakash M Nadkarni, Lucila Ohno-Machado, Wendy W Chapman. (2011) Natural language processing: an introduction, Journal of the American Medical Informatics Association, Volume 18, Issue 5, Pages 544–551
10. Cambria, E., & White, B. (2014) Jumping NLP curves: A review of natural language processing research. IEEE Computational intelligence magazine, 9(2), 48-57.
11. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017) Attention is all you need. Advances in neural information processing systems, 30.
12. Poria, S., Majumder, N., Mihalcea, R., & Hovy, E. (2019) Emotion recognition in conversation: Research challenges, datasets, and recent advances. IEEE Access, 7, 100943-100953.
13. Jia Xibin, Li Rang, Hu Changjian, Chen Juncheng. (2017) A Review of Research on Intelligent Dialogue Systems.
14. Gao, J., Galley, M., & Li, L. (2019) Neural approaches to conversational AI: Question answering, task-oriented dialogues and social chatbots. Now Foundations and Trends.
15. Serban, I., Sordoni, A., Bengio, Y., Courville, A., & Pineau, J. (2016) Building end-to-end dialogue systems using generative hierarchical neural network models. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 30, No. 1).
16. Sutskever, I., Vinyals, O., & Le, Q. V. (2014) Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27.
17. Eli Collins. (2021) LaMDA: our breakthrough conversation technology. Google. Retrieved July 3, 2022 from https://blog.google/technology/ai/lamda/
18. Li Zhou, Jianfeng Gao, Di Li, Heung-Yeung Shum. (2020) The Design and Implementation of XiaoIce, an Empathetic Social Chatbot. Computational Linguistics 46 (1): 53–93.
19. Jiqizhixin (2021) How long is there to go for the next generation of intelligent dialogue systems that speak as naturally and fluently as people?. Retrieved July 3, 2022 from https://www.jiqizhixin.com/articles/2021-04-27-6
20. Jaehun Jung, Bokyung Son, and Sungwon Lyu. (2020) AttnIO: Knowledge Graph Exploration with In-and-Out Attention Flow for Knowledge-Grounded Dialogue. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP), pp. 3484–3497, Online. Association for Computational Linguistics.
21. Zheng, Y., Zhang, R., Huang, M., & Mao, X. (2020, April) A pre-training based personalized dialogue generation model with persona-sparse data. In Proceedings of the AAAI Conference on Artificial Intelligence.Vol. 34, No. 05, pp. 9693-9700.
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).