Review of State-of-the-Art Design Techniques for Chatbots
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REVIEW ARTICLE
Review of State‑of‑the‑Art Design Techniques for Chatbots Ritu Agarwal1 · Mani Wadhwa1 Received: 7 April 2020 / Accepted: 15 July 2020 © Springer Nature Singapore Pte Ltd 2020
Abstract Amazon’s Alexa, Apple’s Siri, Google Assistant and Microsoft’s Cortana, clearly illustrate the impressive research work and potentials to be explored in the field of conversational agents. Conversational agent, chatter-bot or chatbot is a program expected to converse with near-human intelligence. Chatbots are designed to be used either as task-oriented ones or simply open-ended dialogue generator. Many approaches have been proposed in this field which ranges from earlier versions of hard-coded response generator to the advanced development techniques in Artificial Intelligence. In a broader sense, these can be categorized as rule-based and neural network based. While rule-based relies on predefined templates and responses, a neural network based relies on deep learning models. Rule-based are preferable for simpler task-oriented conversations. Open-domain conversational modeling is a more challenging area and uses mostly neural network-based approaches. This paper begins with an introduction of chatbots, followed by in-depth discussion on various classical or rule-based and neuralnetwork-based approaches. The evaluation metrics employed for chatbots are mentioned. The paper concludes with a table consisting of recent research done in the field. It covers all the latest and significant publications in the field, the evaluation metrics employed, the corpus which is used as well as the possible areas of enhancement that exist in the proposed techniques. Keywords AIML · Recurrent Neural Network · LSTM · Deep seq2seq · HRED
Introduction In 1950, Alan Turing posed a question, Can machines think? [1] From that time onwards, a challenge has been posed to Artificial Intelligence practitioners to make machines think or in simple words disguise it as a human. Chatbots came into the picture as a utility program, an advisor or simply a friend with whom you can talk to. There are various design techniques which emerged during its evolution. This paper deals particularly with the techniques used to build chatbots and their respective chatbot example. The primary task of a chatbot is to produce a suitable response by contemplating natural language input provided by humans. There are several ways to generate that response, This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar”. * Mani Wadhwa [email protected] 1
Department of Information Technology, Delhi Technological University, Delhi 110042, India
which defines the modeling mechanism of a chatbot as shown in Fig. 1. One is the rule-based method, wherein clever parsing of user input with hardcoded phrases and premade templates are used to generate the reply. The other one, neural-network-based approach was made po
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