Question Answering Chatbot Using Memory Networks

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Author(s): Riddhi Patel (Charotar University of Science and Technology, India), Parth Patel (Charotar University of Science and Technology, India), Chintan M. Bhatt (Charotar University of Science and Technology, India)and Mrugendrasinh L. Rahevar (Charotar University of Science and Technology, India)
Copyright: 2021
Pages: 13
Source title: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
Source Author(s)/Editor(s): Valentina Chkoniya (University of Aveiro, Portugal)
DOI: 10.4018/978-1-7998-6985-6.ch019

Keywords: Artificial Intelligence / Data Management / Information Science Reference / Knowledge Management and Library Science

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Abstract

Manipulation of a large amount of data becomes a very tedious task. Hence, the authors took the approach of memory networks for the implementation of the chatbot. Traditionally, the LSTM model was used to implement chatbots and QA systems. But the LSTM failed to store relevant information when given a longer information set. On the contrary, the memory networks have an additional memory component with it. This can help in storing long information for further use which is greatly advantageous for the QA and chatbot systems as compared to LSTM. The authors trained and tested their model over Facebook's bAbi dataset which consists of several tasks and has questions regarding each task to retrieve the accuracy of the model. On the pedestal of that dataset, they have presented the accuracy for every task in their study with memory networks.

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