Abstract
Chatbots are computer programs capable to carry a conversation with human. They can be seen as an artificial agent designed to serve the purpose of conversation with the end user. Chatbots are gaining popularity especially in business and health sector as they have the potential to automate service and reduce human efforts. Widespread use of Apps, maturation of Artificial Intelligence (AI) technologies and integration of Natural Language Processing (NLP) fuels up the growth of chatbot. In this paper, we present different models of chatbots along with an architectural overview of computationally intelligent chatbot in context of recent technologies. In the three layer architecture, we have given insights of how the NLP, Natural Language Understanding (NLU) and Decision engine work together with Knowledge Base to achieve AI using Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM). In addition, we also discuss different chatbot platforms and development frameworks of recent times. Our core emphasis is on analysis of recent development approaches of textbased conversational systems. We identify few challenges in intelligent chatbot development that may be helpful for future research works.
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References
Ramesh, K., Ravishankaran, S., Joshi, A., Chandrasekaran, K.: A survey of design techniques for conversational agents. In: Kaushik, S., Gupta, D., Kharb, L., Chahal, D. (eds.) ICICCT 2017. CCIS, vol. 750, pp. 336–350. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6544-6_31
Machinery, C.: Computing machinery and intelligence-AM turing. Mind 59(236), 433 (1950)
Weizenbaum, J.: Elizaa computer program for the study of natural language communication between man and machine. Commun. ACM 9(1), 36–45 (1966)
Perez-Marin, D.: Conversational Agents and Natural Language Interaction: Techniques and Effective Practices: Techniques and Effective Practices. IGI Global (2011)
Mauldin, M.L.: Chatterbots, tinymuds, and the turing test: entering the loebner prize competition. In: AAAI, vol. 94, pp. 16–21 (1994)
McTear, M.F.: Spoken Dialogue Technology: Toward the Conversational User Interface. Springer, London (2004). https://doi.org/10.1007/978-0-85729-414-2
Lester, J., Branting, K., Mott, B.: Conversational agents. In: The Practical Handbook of Internet Computing, pp. 220–240 (2004)
Wallace, R.: The elements of AIML style. Alice AI Foundation (2003)
Marietto, M.D.G.B., et al.: Artificial Intelligence Markup Language: A Brief Tutorial. CoRR abs/1307.3091 (2013)
Chowdhury, G.G.: Natural language processing. Ann. Rev. Inf. Sci. Technol. 37(1), 51–89 (2003)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3(Feb), 1137–1155 (2003)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Chen, H., Liu, X., Yin, D., Tang, J.: A survey on dialogue systems: Recent advances and new frontiers. arXiv preprint arXiv:1711.01731 (2017)
Deshpande, A., Shahane, A., Gadre, D., Deshpande, M., Joshi, P.M.: A survey of various chatbot implementation techniques. Int. J. Comput. Eng. Appl. 11 (2017). ISSN 2321-3469
Mobgea: The Power of Chatbots: The art of Conversation. White Paper (2017)
Shah, V.: Autopsy of a Chatbot: The 7 core components needed for a successful implementation (2017). https://medium.com/@vihangshah/the-magnificent-7-core-components-needed-for-a-successful-implementation-7b4e0d723e33
Kang, A.: Understanding the Differences Between Alexa, Api.ai, Wit.ai, and LUIS (2017). https://medium.com/@abraham.kang/understanding-the-differences-between-alexa-api-ai-wit-ai-and-luis-cortana-2404ece0977c
Berg, M.M.: Modelling of natural dialogues in the context of speech-based information and control systems (2014)
Bruni, E., Fernandez, R.: Adversarial evaluation for open-domain dialogue generation. In: Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pp. 284–288 (2017)
Shen, X., et al.: A conditional variational framework for dialog generation. arXiv preprint arXiv:1705.00316 (2017)
Williams, J., Raux, A., Ramachandran, D., Black, A.: The dialog state tracking challenge. In: Proceedings of the SIGDIAL 2013 Conference, pp. 404–413 (2013)
Socher, R., Huang, E.H., Pennin, J., Manning, C.D., Ng, A.Y.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Advances in Neural Information Processing Systems, pp. 801–809 (2011)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Schwenk, H.: Continuous space translation models for phrase-based statistical machine translation. In: Proceedings of COLING 2012: Posters, pp. 1071–1080 (2012)
Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)
Abolafia, D.: A Recurrent Neural Network Music Generation Tutorial (2017). https://magenta.tensorflow.org
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Srivastava, P.: Essentials of Deep Learning: Introduction to Long Short Term Memory (2017). https://www.analyticsvidhya.com/
Neubig, G.: Neural machine translation and sequence-to-sequence models: a tutorial. arXiv preprint arXiv:1703.01619 (2017)
Chablani, M.: Sequence to sequence model: Introduction and concepts (2017). https://towardsdatascience.com
Zhou, H., Huang, M., et al.: Context-aware natural language generation for spoken dialogue systems. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2032–2041 (2016)
Dušek, O., Jurčíček, F.: Sequence-to-sequence generation for spoken dialogue via deep syntax trees and strings. arXiv preprint arXiv:1606.05491 (2016)
Dušek, O., Jurčíček, F.: A context-aware natural language generator for dialogue systems. arXiv preprint arXiv:1608.07076 (2016)
Zamanirad, S., Benatallah, B., Chai Barukh, M., Casati, F., Rodriguez, C.: Programming bots by synthesizing natural language expressions into API invocations. In: Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, pp. 832–837. IEEE Press (2017)
Rahman, A., Al Mamun, A., Islam, A.: Programming challenges of chatbot: current and future prospective. In: 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 75–78. IEEE (2017)
Couto, J.: Building a Chatbot: Analysis and limitations of modern platforms (2017). https://tryolabs.com/blog/2017/01/25/building-a-chatbot-analysis–limitations-of-modern-platforms
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Borah, B., Pathak, D., Sarmah, P., Som, B., Nandi, S. (2019). Survey of Textbased Chatbot in Perspective of Recent Technologies. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1031. Springer, Singapore. https://doi.org/10.1007/978-981-13-8581-0_7
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