Performance analysis and comparison of representative chatbots based on deep learning

Research Article
Open access

Performance analysis and comparison of representative chatbots based on deep learning

Runpu Wang 1*
  • 1 Shanghai Jiao Tong University, Shanghai, Minhang District, 201100, China    
  • *corresponding author sjtu-walter@sjtu.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230945
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

In today's society, chat robots have entered people's vision. They can mainly carry out corresponding human-computer interaction, and are also research hotspots in the field of science and technology. Chat robots can build models to understand the input content, and then output relatively natural answers. In recent years, their birth has also produced a certain range of applications. However, the most representative chat robots are mainly based on retrieval and generation. Their patterns are different, so their behavior is also different. At the same time, in order to enable users to choose better chat robots, this paper compares the performance of retrieval chat robots based on SMN model, retrieval chat robots based on DAM model and seq2seq generation chat robots respectively. Their performance was analyzed and evaluated, mainly from the appropriateness and diversity of their responses and the difficulty of the training model.

Keywords:

Chatbot, Seq2Seq, SMN, DAM, Natural language processing

Wang,R. (2023). Performance analysis and comparison of representative chatbots based on deep learning. Applied and Computational Engineering,6,731-737.
Export citation

References

[1]. Zongcheng Ji, Zhengdong Lu, and Hang Li. 2014. An information retrieval approach to short text conversation. arXiv preprint arXiv:1408.6988

[2]. Yu Wu, Wei Wu, Chen Xing, Ming Zhou, and Zhoujun Li. 2017. Sequential Match- ing Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 496–505, Vancouver, Canada. Association for Computational Linguistics.

[3]. Wang Lubao Research on end-to-end task-based dialogue system based on deep learning [D]. Changchun University of Technology, 2022. DOI: 10.27805/d.cnki.gccgy.2022.000447.

[4]. Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555

[5]. Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classifification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

[6]. Wang Kexin Research and Implementation of Intelligent Chat Robot Based on Deep Learning [D]. Heilongjiang University, 2021. DOI: 10.27123/d.cnki.ghlju.2021.000387.

[7]. Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International Conference on Machine Learning, pages 2048–2057.

[8]. Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, Wei-Ying Ma. 2016. Topic Aware Neural Response Generation. arXiv preprint arXiv:1606.08340

[9]. Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv preprint arXiv:1406.1078

[10]. Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung. 2015. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. arXiv preprint arXiv:1506.04214


Cite this article

Wang,R. (2023). Performance analysis and comparison of representative chatbots based on deep learning. Applied and Computational Engineering,6,731-737.

Data availability

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

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN:978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor:Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN:2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series 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 series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series'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 series.
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 policy for details).

References

[1]. Zongcheng Ji, Zhengdong Lu, and Hang Li. 2014. An information retrieval approach to short text conversation. arXiv preprint arXiv:1408.6988

[2]. Yu Wu, Wei Wu, Chen Xing, Ming Zhou, and Zhoujun Li. 2017. Sequential Match- ing Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 496–505, Vancouver, Canada. Association for Computational Linguistics.

[3]. Wang Lubao Research on end-to-end task-based dialogue system based on deep learning [D]. Changchun University of Technology, 2022. DOI: 10.27805/d.cnki.gccgy.2022.000447.

[4]. Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555

[5]. Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classifification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

[6]. Wang Kexin Research and Implementation of Intelligent Chat Robot Based on Deep Learning [D]. Heilongjiang University, 2021. DOI: 10.27123/d.cnki.ghlju.2021.000387.

[7]. Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International Conference on Machine Learning, pages 2048–2057.

[8]. Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, Wei-Ying Ma. 2016. Topic Aware Neural Response Generation. arXiv preprint arXiv:1606.08340

[9]. Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv preprint arXiv:1406.1078

[10]. Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung. 2015. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. arXiv preprint arXiv:1506.04214