![]() Socher, R., Huang, E.H., Pennin, J., Manning, C.D., Ng, A.Y.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Proceedings of the SIGDIAL 2013 Conference, pp. Williams, J., Raux, A., Ramachandran, D., Black, A.: The dialog state tracking challenge. Shen, X., et al.: A conditional variational framework for dialog generation. In: Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pp. M.M.: Modelling of natural dialogues in the context of speech-based information and control systems (2014)īruni, E., Fernandez, R.: Adversarial evaluation for open-domain dialogue generation. A.: Understanding the Differences Between Alexa, Api.ai, Wit.ai, and LUIS (2017). Shah, V.: Autopsy of a Chatbot: The 7 core components needed for a successful implementation (2017). Mobgea: The Power of Chatbots: The art of Conversation. arXiv preprint arXiv:1711.01731 (2017)ĭeshpande, A., Shahane, A., Gadre, D., Deshpande, M., Joshi, P.M.: A survey of various chatbot implementation techniques. arXiv preprint arXiv:1406.1078 (2014)Ĭhen, H., Liu, X., Yin, D., Tang, J.: A survey on dialogue systems: Recent advances and new frontiers. arXiv preprint arXiv:1409.0473 (2014)Ĭho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. 3(Feb), 1137–1155 (2003)īahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. 3104–3112 (2014)īengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. In: Advances in Neural Information Processing Systems, pp. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. CoRR abs/1307.3091 (2013)Ĭhowdhury, G.G.: Natural language processing. Marietto, M.D.G.B., et al.: Artificial Intelligence Markup Language: A Brief Tutorial. ![]() In: The Practical Handbook of Internet Computing, pp. Lester, J., Branting, K., Mott, B.: Conversational agents. McTear, M.F.: Spoken Dialogue Technology: Toward the Conversational User Interface. Mauldin, M.L.: Chatterbots, tinymuds, and the turing test: entering the loebner prize competition. Perez-Marin, D.: Conversational Agents and Natural Language Interaction: Techniques and Effective Practices: Techniques and Effective Practices. Weizenbaum, J.: Elizaa computer program for the study of natural language communication between man and machine. Machinery, C.: Computing machinery and intelligence-AM turing. In: Kaushik, S., Gupta, D., Kharb, L., Chahal, D. Ramesh, K., Ravishankaran, S., Joshi, A., Chandrasekaran, K.: A survey of design techniques for conversational agents. We identify few challenges in intelligent chatbot development that may be helpful for future research works. Our core emphasis is on analysis of recent development approaches of textbased conversational systems. In addition, we also discuss different chatbot platforms and development frameworks of recent times. 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 this paper, we present different models of chatbots along with an architectural overview of computationally intelligent chatbot in context of recent technologies. ![]() Widespread use of Apps, maturation of Artificial Intelligence (AI) technologies and integration of Natural Language Processing (NLP) fuels up the growth of chatbot. Chatbots are gaining popularity especially in business and health sector as they have the potential to automate service and reduce human efforts. They can be seen as an artificial agent designed to serve the purpose of conversation with the end user. Chatbots are computer programs capable to carry a conversation with human.
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