Comparative Analysis of Classical and Neural  Networks based ChatBot’s Techniques

Imran ullah khan; junaid javed; Ahthasham Sajid; Shahnoor; Iqra  Tabassum

doi:10.33317/ssurj.508

Authors

Imran ullah khan Hebei University Baoding China
junaid javed 1Department of Computer Science, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan
Ahthasham Sajid
Shahnoor
Iqra Tabassum

DOI:

https://doi.org/10.33317/ssurj.508

Abstract

Conversational agents like Alexa from Amazon, Siri from Apple, Assistant from Google, and Cortana from Microsoft demonstrate extraordinary research and potential in conversational agents. A conversational agent, chatter-bot, or chatbot is a piece of computer software supposed to communicate at a level of intelligence comparable to a person's. Chatbots are designed for various purposes, such as task-oriented helpers and creators of open-ended discourse. Numerous approaches have been studied, from primitive types of hard-coded response generators to contemporary ways of constructing artificial intelligence. These are classified as rule-based or neural network-based systems. Unlike the rule-based technique, which is based on pre-defined templates and responses, the neural network approach is based on deep learning models. Rule-based communication is optimal for more straightforward, task-oriented conversations. Open-domain conversational modeling is a more complicated topic that depends heavily on neural network techniques. This article begins with an overview of chatbots before diving into the specifics of a variety of traditional, rule-based, and neural network-based methods. A table summarising previous field research closes the survey. It looks at the most recent and vital research on the subject, the evaluation instruments used areas for improvement, and the applicability of the proposed methods.

References

Neff, G. (2016). Talking to bots: Symbiotic agency and the case of Tay. International Journal of Communication, 4915-31, p. 10.

Güzeldere, G., & Franchi, S. (1995). Dialogues with colorful “personalities” of early AI. Stanford Humanities Review, 4(2), 161-169.

Alice Al Foundation. (2023). Ask Alice. Retrieved from: http://www.Alicebot.org/

Turing, A. M. (2009). Computing machinery and intelligence (pp. 23-65). Springer Netherlands.

Serban, I. V., Sankar, C., Germain, M., Zhang, S., Lin, Z., Subramanian, S., ... & Bengio, Y. (2017). A deep reinforcement learning chatbot. arXiv preprint arXiv:1709.02349, pp. 1-40.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Image net classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.

Havrylov, S., & Titov, I. (2017). The emergence of language with multi-agent games: Learning to communicate with sequences of symbols. Advances in neural information processing systems, 30.

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

Li, X., Mou, L., Yan, R., & Zhang, M. (2016). Stalematebreaker: A proactive content-introducing approach to automatic human-computer conversation. arXiv preprint arXiv:1604.04358.

Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27.

Song, Y., Yan, R., Li, X., Zhao, D., & Zhang, M. (2016). Two are better than one: An ensemble of retrieval-and generation-based dialog systems. arXiv preprint arXiv:1610.07149.

Clarizia, F., Colace, F., Lombardi, M., Pascale, F., & Santaniello, D. (2018). Chatbot: An education support system for students. In Cyberspace Safety and Security: 10th International Symposium, CSS 2018, Amalfi, Italy, October 29–31, 2018, Proceedings 10 (pp. 291-302). Springer International Publishing.

Colace, F., De Santo, M., Pascale, F., Lemma, S., & Lombardi, M. (2017). BotWheels: a Petri Net-based Chatbot for Recommending Tires. DATA, 17, 350-358.

Edwards, B. I., Muniru, I. O., & Cheok, A. D. (2016). Robots to the rescue: a review of studies on differential medical diagnosis employing ontology-based chatbot technology. Preprints.

Casillo, M., Clarizia, F., D'Aniello, G., De Santo, M., Lombardi, M., & Santaniello, D. (2020). CHAT-Bot: A cultural heritage-aware teller-bot for supporting touristic experiences. Pattern Recognition Letters, 131, 234-243.

Bradeško, L., Starc, J., Mladenic, D., Grobelnik, M., & Witbrock, M. (2016, September). Curious cat is a conversational crowd-based and context-aware knowledge acquisition chatbot. In 2016 IEEE 8th International Conference on Intelligent Systems (IS) (pp. 239-252). IEEE.

Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45.

Ahmad, S. (2007). Tutorial on natural language processing. Artificial Intelligence, 810(161).

Agarwal, R., & Wadhwa, M. (2020). Review of state-of-the-art design techniques for chatbots. SN Computer Science, 1(5), 246.

Bradeško, L., & Mladenić, D. (2012, October). A survey of chatbot systems through a loebner prize competition. In Proceedings of Slovenian language technologies society eighth conference of language technologies (Vol. 2, pp. 34-37). sn.

Hutchens, J. L., & Alder, M. D. (1998). Introducing megahal. In New Methods in Language Processing and Computational Natural Language Learning.

Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Stanford: Stanford Knowl Syst Lab, p. 25.

Lenat, D. B. (1995). CYC: A large-scale investment in knowledge infrastructure. Communications of the ACM, 38(11), 33-38.

Al-Zubaide, H., & Issa, A. A. (2011, November). Ontbot: Ontology-based chatbot. In International Symposium on Innovations in Information and Communications Technology (pp. 7-12). IEEE.

Marietto, M. D. G. B., de Aguiar, R. V., Barbosa, G. D. O., Botelho, W. T., Pimentel, E., França, R. D. S., & da Silva, V. L. (2013). Artificial intelligence markup language: a brief tutorial. arXiv preprint arXiv:1307.3091.

Epstein, R., Roberts, G., & Beber, G. (Eds.). (2009). Parsing the Turing test (pp. 978-1). Dordrecht: Springer Netherlands.

Wilcox, B., Wilcox, S., Psych, B. A., & Arts, D. F. (2010). Suzette, the most human computer. Agent’s Processing, Cognition: https://www. chatbots. org/images/uploads/research_papers/9491. pdf.

Lokman, A. S., & Zain, J. M. (2010). Extension and prerequisite: An algorithm to enable relations between responses in chatbot technology. Journal of Computer Science, 6(10), 1212.

Bradeško, L., & Mladenić, D. (2012, October). A survey of chatbot systems through a loebner prize competition. In Proceedings of Slovenian language technologies society eighth conference of language technologies (Vol. 2, pp. 34-37). sn.

Hochreiter, S. (1998). The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02), 107-116.

Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2017). LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), 2222-2232.

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27.

Yao, K., Peng, B., Zweig, G., & Wong, K. F. (2016). An attentional neural conversation model with improved specificity. arXiv preprint arXiv:1606.01292.

Setiaji, B., & Wibowo, F. W. (2016, January). Chatbot using knowledge in database: human-to-machine conversation modeling. In 2016 7th international conference on intelligent systems, modelling and simulation (ISMS) (pp. 72-77). IEEE.

Abdul-Kader, S. A., & Woods, J. C. (2015). Survey on chatbot design techniques in speech conversation systems. International Journal of Advanced Computer Science and Applications, 6(7).

Mladenić, D., & Bradeško, L. (2012). A survey of chabot system through a Loebner prize competition. Proceedings of Slovenian Artificial Intelligence Laboratory, Jozef Stefan Institute in Ljubljana.

Sharma, S. (2022). What Are The Advantages & Disadvantages of Chatbot in Your Business Retrieved from: https://allusefulinfo.com/s=what+are+advantages+disadvantages+of+Chatbots+in+business

Marietto, M. D. G. B., de Aguiar, R. V., Barbosa, G. D. O., Botelho, W. T., Pimentel, E., França, R. D. S., & da Silva, V. L. (2013). Artificial intelligence markup language: a brief tutorial. arXiv preprint arXiv:1307.3091.

Arsovski, S., Cheok, A. D., Idris, M., & Raffur, M. R. B. A. (2017, September). Analysis of the chatbot open source languages aiml and chatscript: A review. In 9th DQM International Conference on life cycle engineering and management.

Wallace, R. S. (2009). The anatomy of ALICE (pp. 181-210). Springer Netherlands.

Yan, R., Song, Y., & Wu, H. (2016, July). Learning to respond with deep neural networks for retrieval-based human-computer conversation system. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 55-64).

Caldarini, G., Jaf, S., & McGarry, K. (2022). A literature survey of recent advances in chatbots. Information, 13(1), 41.

Satu, M. S., & Parvez, M. H. (2015, November). Review of integrated applications with aiml basedchatbot. In 2015 International Conference on Computer and Information Engineering (ICCIE) (pp.87-90). IEEE.

Satu, M. S., Parvez, M. H., & Mamun, S(2015).Review_of_integrated_applications_with_AIML_based_C

hatbots.Retrievedfrom:https://www.researchgate.net/profile/MdSatu/publication/304063740_Review_of_integrated_applications_with_AIML_based_chatbot/links/5765418a08aedbc345f37e4e/Review-of-integrated-applications-with-AIML-based-chatbot.pdf

Wilcox, B., & Wilcox, S. (2014). Making it real: Loebner-winning chatbot design. ARBOR Ciencia, Pensamiento y Cultura, 189(764), 1-13.

Ramesh, K., Ravishankaran, S., Joshi, A., & Chandrasekaran, K. (2017, October). A survey of design techniques for conversational agents. In Information, Communication and Computing Technology: Second International Conference, ICICCT 2017, New Delhi, India, May 13, 2017, Revised Selected Papers (pp. 336-350). Singapore: Springer Singapore.

Bradeško, L., & Mladenić, D. (2012, October). A survey of chatbot systems through a loebner prize competition. In Proceedings of Slovenian language technologies society eighth conference of language technologies (Vol. 2, pp. 34-37). sn.

Ahmad, N. A., Che, M. H., Zainal, A., Abd Rauf, M. F., & Adnan, Z. (2018). Review of chatbots design techniques. International Journal of Computer Applications, 181(8), 7-10.

Ehrenbrink, P., Osman, S., & Möller, S. (2017, November). Google now is for the extraverted, Cortana for the introverted: investigating the influence of personality on IPA preference. In Proceedings of the 29th Australian Conference on Computer-Human Interaction

(pp. 257-265).

Muangkammuen, P., Intiruk, N., & Saikaew, K. R. (2018, November). Automated thai-faq chatbot using rnn-lstm. In 2018 22nd International Computer Science and Engineering Conference (ICSEC) (pp. 1-4). IEEE.

Kim, S., Kwon, O. W., & Kim, H. (2020). Knowledge-grounded chatbot based on dual Wasserstein generative adversarial networks with effective attention mechanisms. Applied Sciences, 10(9), 3335.

Qi, W., Ovur, S. E., Li, Z., Marzullo, A., & Song, R. (2021). Multi-sensor guided hand gesture recognition for a teleoperated robot using a recurrent neural network. IEEE Robotics and Automation Letters, 6(3), 6039-6045.

Patil, S., Mudaliar, V. M., Kamat, P., & Gite, S. (2020). LSTM-based Ensemble Network to enhance the learning of long-term dependencies in the chatbot. International Journal for Simulation and Multidisciplinary Design Optimization, 11, 25.

Mathur, S., & Lopez, D. (2019). A scaled‐down neural conversational model for chatbots. Concurrency and Computation: Practice and Experience, 31(10), e4761.

Sojasingarayar, A. (2020). Seq2seq ai chatbot with an attention mechanism. arXiv preprint arXiv:2006.02767.

geeks for geeks. (2022). seq2seq-model-in-machine-learning. Retrieved from:https://www.geeksforgeeks.org/seq2seq-model-in machine-learning/

Csaky, R. (2019). Deep learning-based chatbot models. arXiv preprint arXiv:1908.08835.

Serban, I., Sordoni, A., Lowe, R., Charlin, L., Pineau, J., Courville, A., & Bengio, Y. (2017, February). A hierarchical latent variable encoder-decoder model for generating dialogues. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).

Github. (2022). Lecture 8 – Translation, Seq2Seq, Attention. Retrieved from: https://x-wei.github.io/xcs224nlecture8.html#nmt-advantages-disadvantages_1.

softwaretestinghelp . (2023). Top 12 Best AI Chatbots For 2023 [Review & Comparison]. Retrieved from: https://www.softwaretestinghelp.com/best-ai-Chatbots

Madhumitha, S., Keerthana, B., & Hemalatha, B. (2020). Interactive Chatbots Using AIML. Retrieved from: https://www.ijana.in/papers/62.pdf

Colace, F., De Santo, M., Lombardi, M., Pascale, F., Pietrosanto, A., & Lemma, S. (2018). Chatbot for e-learning: A case of study. International Journal of Mechanical Engineering and Robotics Research, 7(5), 528-533.

The Medical Futurist. (2021). The Top 12 Health Chatbots. The Medical Futurist, p. 106–111. Retrieved from: https://medicalfuturist.com/top-12-health-chatbots/

Venkat, k. (2020). Examples and Best use cases of chatbots in the Banking Industry. Retrieved from: https://medium.com/voice-tech podcast/examples-and-best-use-cases-of-chatbots-in-banking industry-1a46cebdb0c