Studies advanced in chatbots based on deep learning

Research Article
Open access

Studies advanced in chatbots based on deep learning

Lixin Li 1*
  • 1 Franklin and Marshall College, Lancaster, Pennsylvania, 17603, US    
  • *corresponding author lli1@fandm.edu
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230921
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

Chatbots have always been a hot research topic in the field of human-computer interaction research, which aims to build a conversational intelligent response model to simulate human dialogue. Thanks to the rapid development of natural language processing technology and the continuous accumulation of dialogue data, the research of chat robots have made remarkable progress, which has gradually been widely used in various fields such as e-commerce and smart home. According to different technical frameworks, existing chatbots are mainly divided into two types: retrieval chatbots and generative chatbots. As the primary means of implementing chatbots in the industry, retrieval chatbots have smooth responses and low computational resource consumption. In contrast, generative chatbots do not require a predefined knowledge base and can dynamically generate responses based on the dialogue content. In this paper, focusing on the above two types of frameworks, we introduce the latest research progress in the field of deep learning-based chatbots in detail, including the representative algorithms and corresponding pipelines. Second, we compare the performance of representative algorithms on different datasets. We also summarize the problems chatbot technology research faces and give an outlook on its future development trends.

Keywords:

Natural Language Processing, Chatbot, Deep Learning.

Li,L. (2023). Studies advanced in chatbots based on deep learning. Applied and Computational Engineering,6,678-683.
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References

[1]. Adamopoulou, Eleni, and Lefteris Moussiades. “An Overview of Chatbot Technology.” IFIP Advances in Information and Communication Technology, 2020, pp. 373–383., https://doi.org/10.1007/978-3-030-49186-4_31.

[2]. Weizenbaum, Joseph. “Eliza—a Computer Program for the Study of Natural Language Communication between Man and Machine (1966).” Ideas That Created the Future, 2021, pp. 271–278., https://doi.org/10.7551/mitpress/12274.003.0029.

[3]. Akma, Nahdatul, et al. “Review of Chatbots Design Techniques.” International Journal of Computer Applications, vol. 181, no. 8, 2018, pp. 7–10., https://doi.org/10.5120/ijca2018917606.

[4]. Ji Z, Lu Z, Li H. “An information retrieval approach to short text conversation[J]”. arXiv preprint arXiv:1408.6988, 2014.

[5]. Yan Z, Duan N, Bao J, et al. Docchat. “An information retrieval approach for chatbot engines using unstructured documents[C]”//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016: 516-525.

[6]. Jung, Sangkeun. “Semantic Vector Learning for Natural Language Understanding.” Computer Speech & Language, vol. 56, 2019, pp. 130–145., https://doi.org/10.1016/j.csl.2018.12.008.

[7]. Ritter A, Cherry C, Dolan B. “Data-driven response generation in social media[C]”//Empirical Methods in Natural Language Processing (EMNLP). 2011.

[8]. Vinyals O, Le Q. “A neural conversational model[J]”. arXiv preprint arXiv:1506.05869, 2015.

[9]. Shang L, Lu Z, Li H. “Neural responding machine for short-text conversation[J]”. arXiv preprint arXiv:1503.02364, 2015.

[10]. Serban I, Sordoni A, Bengio Y, et al. “Building end-to-end dialogue systems using generative hierarchical neural network models[C]”//Proceedings of the AAAI Conference on Artificial Intelligence. 2016, 30(1).

[11]. Zhou, Xiangyang, et al. “Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network.” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, https://doi.org/10.18653/v1/p18-1103.

[12]. Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2019. Towards empathetic open- domain conversation models: A new benchmark and dataset. In Proceedings of the 57th Confer- ence of the Association for Computational Linguis- tics, pages 5370–5381, Florence, Italy. Association for Computational Linguistics.

[13]. Ricardo Baeza-Yates, Berthier Ribeiro-Neto, et al. 1999. Modern information retrieval, volume 463. ACM press New York.

[14]. Ellen M Voorhees et al. 1999. The trec-8 question an- swering track report. In Trec, pages 77–82.

Cite this article

Li,L. (2023). Studies advanced in chatbots based on deep learning. Applied and Computational Engineering,6,678-683.

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 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)

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References

[1]. Adamopoulou, Eleni, and Lefteris Moussiades. “An Overview of Chatbot Technology.” IFIP Advances in Information and Communication Technology, 2020, pp. 373–383., https://doi.org/10.1007/978-3-030-49186-4_31.

[2]. Weizenbaum, Joseph. “Eliza—a Computer Program for the Study of Natural Language Communication between Man and Machine (1966).” Ideas That Created the Future, 2021, pp. 271–278., https://doi.org/10.7551/mitpress/12274.003.0029.

[3]. Akma, Nahdatul, et al. “Review of Chatbots Design Techniques.” International Journal of Computer Applications, vol. 181, no. 8, 2018, pp. 7–10., https://doi.org/10.5120/ijca2018917606.

[4]. Ji Z, Lu Z, Li H. “An information retrieval approach to short text conversation[J]”. arXiv preprint arXiv:1408.6988, 2014.

[5]. Yan Z, Duan N, Bao J, et al. Docchat. “An information retrieval approach for chatbot engines using unstructured documents[C]”//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016: 516-525.

[6]. Jung, Sangkeun. “Semantic Vector Learning for Natural Language Understanding.” Computer Speech & Language, vol. 56, 2019, pp. 130–145., https://doi.org/10.1016/j.csl.2018.12.008.

[7]. Ritter A, Cherry C, Dolan B. “Data-driven response generation in social media[C]”//Empirical Methods in Natural Language Processing (EMNLP). 2011.

[8]. Vinyals O, Le Q. “A neural conversational model[J]”. arXiv preprint arXiv:1506.05869, 2015.

[9]. Shang L, Lu Z, Li H. “Neural responding machine for short-text conversation[J]”. arXiv preprint arXiv:1503.02364, 2015.

[10]. Serban I, Sordoni A, Bengio Y, et al. “Building end-to-end dialogue systems using generative hierarchical neural network models[C]”//Proceedings of the AAAI Conference on Artificial Intelligence. 2016, 30(1).

[11]. Zhou, Xiangyang, et al. “Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network.” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, https://doi.org/10.18653/v1/p18-1103.

[12]. Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2019. Towards empathetic open- domain conversation models: A new benchmark and dataset. In Proceedings of the 57th Confer- ence of the Association for Computational Linguis- tics, pages 5370–5381, Florence, Italy. Association for Computational Linguistics.

[13]. Ricardo Baeza-Yates, Berthier Ribeiro-Neto, et al. 1999. Modern information retrieval, volume 463. ACM press New York.

[14]. Ellen M Voorhees et al. 1999. The trec-8 question an- swering track report. In Trec, pages 77–82.

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