The Latest Progress in Human-Computer Dialogue System
DOI:
https://doi.org/10.54097/gw3z2h19Keywords:
Human-Computer dialogue system, pipeline method, end-to-end method, non-task-oriented dialogue system.Abstract
The Human-Computer dialogue system allows a computer to make conversations with humans through natural languages, and some designs can accomplish tasks given by humans. The development of the Human-Computer dialogue system can significantly expand the range of users for computers. The profound ELIZA Program designed scripts containing keywords and their corresponding rule-based sentence transformation to respond to the user. As the computation power increases, Human-Computer dialogue systems are handling more varied and complicated scenarios by introducing deeper and more complex artificial neural networks. The designer of a task-oriented system can choose between the pipeline method and the end-to-end method. The pipeline method is basically a pipeline that lets the user input go through three parts handling Natural Language Understanding, Dialogue Management, and Natural Language Generation, to generate an appropriate response back to the user. The end-to-end method, however, uses joint models to allow the parts to interact with others and get more efficient in handling the information. With the development of ChatGPT, more general language models and more varied methodologies are becoming more prevalent in dialogue systems.
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References
J. Weizenbaum, “Eliza—a computer program for the study of natural language communication between man and machine,” Communications of the ACM, vol. 9, no. 1, pp. 36–45, 1966.
Y. Y. Zhao, Z. Y. Wang, P. Wang, T. Yang, R. Zhang, and K. Yi, “A survey on task-oriented dialogue systems,” Chinese Journal of Computers, vol. 43, no. 10, pp. 1862–1896, 2020.
X. Wang and C. Yuan, “Recent advances on human-computer dialogue,” CAAI Transactions on Intelligence Technology, vol. 1, no. 4, pp. 303–312, 2016.
R. Navigli, “Natural language understanding: Instructions for (present and future) use.” in IJCAI, vol. 18, 2018, pp. 5697–5702.
P. Semaan, “Natural language generation: an overview,” J Comput Sci Res, vol. 1, no. 3, pp.50–57, 2012.
J. P. Chen, J. H. Ma, and Y. J. Wang, “A survey of human-machine dialogue systems based on multiple rounds of interaction,” Journal of Nanjing University of Information Science Technology (Natural Science Edition), vol. 11, no. 3, pp. 256–268, 2019.
P. Haffner, G. Tur, and J. H. Wright, “Optimizing svms for complex call classification,” in 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP’03)., vol. 1. IEEE, 2003, pp. I–I.
R. Sarikaya, G. E. Hinton, and A. Deoras, “Application of deep belief networks for natural language understanding,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 4, pp. 778–784, 2014.
K. Yao, G. Zweig, M.-Y. Hwang, Y. Shi, and D. Yu, “Recurrent neural networks for language understanding.” in Interspeech, 2013, pp. 2524–2528.
G. Mesnil, Y. Dauphin, K. Yao, Y. Bengio, L. Deng, D. Hakkani-Tur, X. He, L. Heck, G. Tur, D. Yu et al., “Using recurrent neural networks for slot filling in spoken language understanding,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 3, pp. 530–539, 2014.
J. Lafferty, A. McCallum, and F. C. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” 2001.
F. Peng and A. McCallum, “Information extraction from research papers using conditional random fields,” Information processing & management, vol. 42, no. 4, pp. 963–979, 2006.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: Continual prediction with lstm,” Neural computation, vol. 12, no. 10, pp. 2451–2471, 2000.
K. Cho, B. Van Merriënboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder approaches,” arXiv preprint arXiv:1409.1259, 2014.
Y. J. Zhao, Y. L. Li, and M. Lin, “A review of the research on dialogue management of task-oriented systems,” in Journal of Physics: Conference Series, vol. 1267, no. 1. IOP Publishing, 2019, p. 012025.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al., “Human-level control through deep reinforcement learning,” nature, vol. 518, no. 7540, pp. 529–533, 2015.
L. Baptist and S. Seneff, “Genesis-ii: A versatile system for language generation in conversationalsystem applications,” in Sixth international conference on spoken language processing, 2000.
T. Zhao and M. Eskenazi, “Towards end-to-end learning for dialog state tracking and management using deep reinforcement learning,” arXiv preprint arXiv:1606.02560, 2016.
H. Cuayáhuitl, “Simpleds: A simple deep reinforcement learning dialogue system,” Dialogues with Social Robots: Enablements, Analyses, and Evaluation, pp. 109–118, 2017.
O. Vinyals and Q. Le, “A neural conversational model,” arXiv preprint arXiv:1506.05869, 2015.
F. Sun, “Chatgpt, the start of a new era,” 2022.
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