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Towards building a neural conversation chatbot through seq2seq model
K. Khadar Nawas, J. Christy Jackson, , S. Ramanathan,
Published in International Journal of Scientific and Technology Research
2020
Volume: 9
   
Issue: 3
Pages: 1219 - 1222
Abstract
Improvements in computation and processing power paved a way for Machine learning to be applied more efficiently in real-time and in a lot of applications. In which most prominent area is Natural Language Processing and Natural Language Understanding, which helps the computer to process and understands the natural language used by people. Thanks to deep learning models and architectures which made this process of making the system process and understand natural language, which makes the system more intelligent. Chatting agent’s AKA-Chatbot is one of the major use cases of Natural Language Processing and Natural Language Understanding, which can be used in different domains to engage customers and provide a response to customer’s queries. Though many chatbots use a retrieval-based model with the recent advancement of Deep Learning, we in this work use Neural Networks to train a chat model with a question and answer datasets that make models understand the patterns in it and behave intelligently. Here we build a domain-specific generative chatbot using Neural Networks to train a conversational Model which reads the pattern of data and reply answer when a new question is asked. Finally, we conclude by validating how relevant the response generated by the model to test data or test question and provide a further area of improvements to make the system more efficient and intelligent. © 2020 IJSTR.
About the journal
JournalInternational Journal of Scientific and Technology Research
PublisherInternational Journal of Scientific and Technology Research
ISSN22778616