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- Vol. 4 Issue 2
- Article
A Behavioral Chatbot Using Encoder Decoder Architecture
Authors
1Dept.of Computer Science and Engineering, Bannari Amman Institute of Technology, Tamil Nadu, India. 2Dept.of Biomedical Engineering, Bannari Amman Institute of Technology, Tamil Nadu, India. 3Dept.of Aeronautical Engineering, Bannari Amman Institute of Technology, Tamil Nadu, India.
Article Information
- DOI
- No DOI
- Published In
- Volume 4, Issue 2 (2023)
- Published Online
- March-April 2023
- Pages
- 85-89
- Article Type
- Research Article
- Check for updates
- No DOI available for cross-reference
References
1. Jagdish Singh et al 2019 J. Phys.: Conf. Ser. 1228 012060 J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2.
Oxford: Clarendon, 1892, pp.68–73.
2. J. Zhang, J. Zhang, S. Ma, J. Yang and G. Gui, "Chatbot Design Method Using Hybrid Word Vector Expression Model Based on Real
Telemarketing Data," KSII Transactions on Internet and Information Systems, vol. 14, no. 4, pp. 1400-1418, 2020. DOI: 10.3837/tiis.2020.04.001.
3. Singh, Rupesh et al. “Chatbot using TensorFlow for small Businesses.” 2018 Second International Conference on Inventive
Communication and Computational Technologies (ICICCT) (2018): 1614-1619.
4. Zhang, X.-D. A Matrix Algebra Approach to Artificial Intelligence; Springer, 2020; 2. Papatsimouli, M.; Lazaridis, L.; Kollias, K.-F.;
Skordas, I.; Fragulis, G.F. Speak with Signs
5. Bengfort, B.; Bilbro, R.; Ojeda, T. Applied Text Analysis with Python: Enabling LanguageAware Data Products with Machine
Learning; O’Reilly Media, Inc.,2018; ISBN 978-1-4919- 6299-2. 15. Nadeau, D.; Sekine, S. A Survey of Named Entity Recognition and
Classification. Lingvisticae Investigationes 2007, 30,3–26
6. A. Adikari, D. de Silva, H. Moraliyage et al.,“Empathic conversational agents for real-time monitoring and co-facilitation of patientcentered healthcare,” Future Generation Computer Systems, vol. 126, pp. 318–329, 2022.
7. J. Wang, X. Sun, and M. Wang, “Emotional conversation generation with bilingual interactive decoding,” IEEE Transactions on
Computational Social Systems, vol. 9, no. 3, pp. 818– 829, 2021.
8. L. Wang, D. Wang, F. Tian et al., “CASS: towards building a social-support chatbot for online health community,” in Proceedings of
the ACM on Human-Computer Interaction, 5(CSCW1), 2021.
9. D. Griol, A. Sanchis, J. M. Molina, and Z. Callejas, “Developing enhanced conversational agents for social virtual worlds,”
Neurocomputing, vol. 354, pp.27–40, 2020.
10. J. Hu, Y. Huang, X. Hu, and Y. Xu, “Enhancing the perceived emotional intelligence of conversational agents through acoustic cues,” in Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, May 2021.
11. J. Wu, S. Ghosh, M. Chollet, S. Ly, S. Mozgai, and S. Scherer, “NADiA - towards neural network driven virtual human conversation
agents,”in Proceedings of the 18th International Conference on Intelligent Virtual Agents, pp. 2262–2264, Sydney, Australia, November
2021.
Oxford: Clarendon, 1892, pp.68–73.
2. J. Zhang, J. Zhang, S. Ma, J. Yang and G. Gui, "Chatbot Design Method Using Hybrid Word Vector Expression Model Based on Real
Telemarketing Data," KSII Transactions on Internet and Information Systems, vol. 14, no. 4, pp. 1400-1418, 2020. DOI: 10.3837/tiis.2020.04.001.
3. Singh, Rupesh et al. “Chatbot using TensorFlow for small Businesses.” 2018 Second International Conference on Inventive
Communication and Computational Technologies (ICICCT) (2018): 1614-1619.
4. Zhang, X.-D. A Matrix Algebra Approach to Artificial Intelligence; Springer, 2020; 2. Papatsimouli, M.; Lazaridis, L.; Kollias, K.-F.;
Skordas, I.; Fragulis, G.F. Speak with Signs
5. Bengfort, B.; Bilbro, R.; Ojeda, T. Applied Text Analysis with Python: Enabling LanguageAware Data Products with Machine
Learning; O’Reilly Media, Inc.,2018; ISBN 978-1-4919- 6299-2. 15. Nadeau, D.; Sekine, S. A Survey of Named Entity Recognition and
Classification. Lingvisticae Investigationes 2007, 30,3–26
6. A. Adikari, D. de Silva, H. Moraliyage et al.,“Empathic conversational agents for real-time monitoring and co-facilitation of patientcentered healthcare,” Future Generation Computer Systems, vol. 126, pp. 318–329, 2022.
7. J. Wang, X. Sun, and M. Wang, “Emotional conversation generation with bilingual interactive decoding,” IEEE Transactions on
Computational Social Systems, vol. 9, no. 3, pp. 818– 829, 2021.
8. L. Wang, D. Wang, F. Tian et al., “CASS: towards building a social-support chatbot for online health community,” in Proceedings of
the ACM on Human-Computer Interaction, 5(CSCW1), 2021.
9. D. Griol, A. Sanchis, J. M. Molina, and Z. Callejas, “Developing enhanced conversational agents for social virtual worlds,”
Neurocomputing, vol. 354, pp.27–40, 2020.
10. J. Hu, Y. Huang, X. Hu, and Y. Xu, “Enhancing the perceived emotional intelligence of conversational agents through acoustic cues,” in Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, May 2021.
11. J. Wu, S. Ghosh, M. Chollet, S. Ly, S. Mozgai, and S. Scherer, “NADiA - towards neural network driven virtual human conversation
agents,”in Proceedings of the 18th International Conference on Intelligent Virtual Agents, pp. 2262–2264, Sydney, Australia, November
2021.
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