An emotion-sensitive dialogue policy for task-oriented dialogue system-Reference-Cited by-同舟云学术

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An emotion-sensitive dialogue policy for task-oriented dialogue system

Published:2024-08-26 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Zhu Hui,Wang Xv,Wang Zhenyu,Xv Kai

Funder

Education and Teaching Reform Project of Finance and Economics in Guangdong Province

Innovation Team Project of Guangdong Electromechanical Vocational and Technical College

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-70463-x.pdf

Reference38 articles.

1. Ni, J., Young, T., Pandelea, V., Xue, F. & Cambria, E. Recent advances in deep learning based dialogue systems: A systematic survey. Artif. Intelligence Rev. 56, 3055–3155 (2023).

2. Zhang, Z., Takanobu, R., Zhu, Q., Huang, M. & Zhu, X. Recent advances and challenges in task-oriented dialog systems. Sci. China Technol. Sci. 63, 2011–2027 (2020).

3. Dai, Y. et al. Learning low-resource end-to-end goal-oriented dialog for fast and reliable system deployment. in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 609–618 ( 2020).

4. Saha, T., Gupta, D., Saha, S. & Bhattacharyya, P. A hierarchical approach for efficient multi-intent dialogue policy learning. Multimed. Tools Appl. 80, 35025–35050 (2021).

5. Li, Z. et al. Guided dialog policy learning without adversarial learning in the loop. arXiv preprint arXiv:2004.03267 ( 2020).

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