Abstract:

Development of Intelligent Conversational Agent using Artificial Intelligence and Machine Learning technique is an interesting problem in the field of natural language processing (NLP). Conversational agents, widely known as chatbots, are being used as virtual assistant in many different domains including business, healthcare and government organizations in attempt to improve the quality of service provided. This paper aims at using NLP towards the development of chatbot to assist the new research scholars. New research scholars often find it difficult where to start, how to start and have some common queries about fundamental concepts in research, research publications, data sources, funding agencies, indexing services etc.  The paper addresses the need of virtual assistant to the new researcher scholars and describes the design of the chatbot model that would help them get the answers to the research queries at their fingertips. The database used in the study is generated by collecting data from web as well as through personal interactions with new research scholars. We have designed retrieval based text chatbot using Keras sequential model with Adagrad optimizer that is optimized for learning rate. The research also reveals the relationship between learning rate and accuracy for five optimizers. It was found that accuracy of Adam and RMSprop optimizers is inversely proportional to the learning rate whereas the accuracy of Adadelta optimizer is directly proportional to the learning rate. The model designed in this work is simple, computationally inexpensive and has succeeded to provide answers to user queries satisfactorily. The paper gives important directions in which research can further be extended.

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