The Advance of Multi-Round Dialogue System with Deep Learning

Research Article
Open access

The Advance of Multi-Round Dialogue System with Deep Learning

Qilin Hu 1 , Yuxuan Yang 2 , Yiming Zhang 3* , Jiaju Zheng 4
  • 1 Beibu Gulf University    
  • 2 Macau University of Science and Technology    
  • 3 Northwest University    
  • 4 ZhengZhou University    
  • *corresponding author zym@stumail.nwu.edu.cn
Published on 1 August 2023 | https://doi.org/10.54254/2755-2721/8/20230299
ACE Vol.8
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-63-8
ISBN (Online): 978-1-915371-64-5

Abstract

Multi-turn dialog systems have seen significant advances in recent years, driven by various approaches. The Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method have all shown promising results in advancing the state-of-the-art in this field. However, challenges remain in developing models that can handle complex and diverse user inputs and generate responses that are not only informative but also engaging and natural. Further research is needed to address these challenges and advance state of art in multi-turn dialogue systems. This paper reviews four critical methods for improving the quality of multi-turn dialogue systems: Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method. The Multi-layer Semantic Method utilizes multi-layer neural networks to model dialogue context and generate responses with improved coherence and relevance. The reinforcement Learning Method employs a reward-based approach to optimize response generation by training models to maximize long-term dialogue success. The knowledge Graph Method incorporates external knowledge sources, such as knowledge graphs, to enrich the dialogue context and improve response quality. The Medicine Knowledge Graph Method focuses on integrating medical knowledge into dialogue systems for healthcare applications. Each of these methods has demonstrated promising results in enhancing the quality of multi-turn dialogue systems.

Keywords:

multi-turn dialogue, natural language processing, deep learning, knowledge graph, reinforcement learning

Hu,Q.;Yang,Y.;Zhang,Y.;Zheng,J. (2023). The Advance of Multi-Round Dialogue System with Deep Learning. Applied and Computational Engineering,8,693-700.
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References

[1]. Zhao, M., Wang, L., Jiang, Z., Li, R., Lu, X., & Hu, Z. (2023). Multi-task learning with graph attention networks for multi-domain task-oriented dialogue systems. Knowledge-Based Systems, 259, 110069.

[2]. Wang, S., Wang, S., Liu, Z., & Zhang, Q. (2023). A role distinguishing Bert model for medical dialogue system in sustainable smart city. Sustainable Energy Technologies and Assessments, 55, 102896.

[3]. Firdaus, M., Ekbal, A., & Cambria, E. (2023). Multitask learning for multilingual intent detection and slot filling in dialogue systems. Information Fusion, 91, 299-315.

[4]. Zhang, W., Cui, Y., Zhang, K., Wang, Y., Zhu, Q., Li, L., & Liu, T. (2023). A Static and Dynamic Attention Framework for Multi Turn Dialogue Generation. ACM Transactions on Information Systems, 41(1), 1-30.

[5]. Wang, X., Zhang, H., Zhao, S., Chen, H., Cheng, B., Ding, Z., ... & Lan, Y. (2023). HiBERT: Detecting the illogical patterns with hierarchical BERT for multi-turn dialogue reasoning. Neurocomputing, 524, 167-177.

[6]. Wang, H., Guo, B., Liu, J., Ding, Y., & Yu, Z. (2023). Towards Informative and Diverse Dialogue Systems over Hierarchical Crowd Intelligence Knowledge Graph. ACM Transactions on Knowledge Discovery from Data.

[7]. Deng, J., Sun, H., Zhang, Z., Cheng, J., & Huang, M. (2023). Recent Advances towards Safe, Responsible, and Moral Dialogue Systems: A Survey. arXiv preprint arXiv:2302.09270.

[8]. Zhen, J. (2018). A Study And Implementation of Multi-level Semantics Model for Multi-turn Dialogue System. [D].

[9]. Sheu, J. S., Wu, S. R., & Wu, W. H. (2023). Performance Improvement on Traditional Chinese Task-Oriented Dialogue Systems with Reinforcement Learning and Regularized Dropout Technique. IEEE Access.

[10]. Liu, Z., Peng, E., Yan, S., Li, G., & Hao, T. (2018, August). T-know: a knowledge graph-based question answering and infor-mation retrieval system for traditional Chinese medicine. In Proceedings of the 27th international conference on computational linguistics: system demonstrations (pp. 15-19).

[11]. Yang, T. H., Pleva, M., Hládek, D., & Su, M. H. (2022, December). BERT-based Chinese Medicine Named Entity Recognition Model Applied to Medication Reminder Dialogue System. In 2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP) (pp. 374-378). IEEE.

Cite this article

Hu,Q.;Yang,Y.;Zhang,Y.;Zheng,J. (2023). The Advance of Multi-Round Dialogue System with Deep Learning. Applied and Computational Engineering,8,693-700.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Zhao, M., Wang, L., Jiang, Z., Li, R., Lu, X., & Hu, Z. (2023). Multi-task learning with graph attention networks for multi-domain task-oriented dialogue systems. Knowledge-Based Systems, 259, 110069.

[2]. Wang, S., Wang, S., Liu, Z., & Zhang, Q. (2023). A role distinguishing Bert model for medical dialogue system in sustainable smart city. Sustainable Energy Technologies and Assessments, 55, 102896.

[3]. Firdaus, M., Ekbal, A., & Cambria, E. (2023). Multitask learning for multilingual intent detection and slot filling in dialogue systems. Information Fusion, 91, 299-315.

[4]. Zhang, W., Cui, Y., Zhang, K., Wang, Y., Zhu, Q., Li, L., & Liu, T. (2023). A Static and Dynamic Attention Framework for Multi Turn Dialogue Generation. ACM Transactions on Information Systems, 41(1), 1-30.

[5]. Wang, X., Zhang, H., Zhao, S., Chen, H., Cheng, B., Ding, Z., ... & Lan, Y. (2023). HiBERT: Detecting the illogical patterns with hierarchical BERT for multi-turn dialogue reasoning. Neurocomputing, 524, 167-177.

[6]. Wang, H., Guo, B., Liu, J., Ding, Y., & Yu, Z. (2023). Towards Informative and Diverse Dialogue Systems over Hierarchical Crowd Intelligence Knowledge Graph. ACM Transactions on Knowledge Discovery from Data.

[7]. Deng, J., Sun, H., Zhang, Z., Cheng, J., & Huang, M. (2023). Recent Advances towards Safe, Responsible, and Moral Dialogue Systems: A Survey. arXiv preprint arXiv:2302.09270.

[8]. Zhen, J. (2018). A Study And Implementation of Multi-level Semantics Model for Multi-turn Dialogue System. [D].

[9]. Sheu, J. S., Wu, S. R., & Wu, W. H. (2023). Performance Improvement on Traditional Chinese Task-Oriented Dialogue Systems with Reinforcement Learning and Regularized Dropout Technique. IEEE Access.

[10]. Liu, Z., Peng, E., Yan, S., Li, G., & Hao, T. (2018, August). T-know: a knowledge graph-based question answering and infor-mation retrieval system for traditional Chinese medicine. In Proceedings of the 27th international conference on computational linguistics: system demonstrations (pp. 15-19).

[11]. Yang, T. H., Pleva, M., Hládek, D., & Su, M. H. (2022, December). BERT-based Chinese Medicine Named Entity Recognition Model Applied to Medication Reminder Dialogue System. In 2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP) (pp. 374-378). IEEE.

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