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Graph and Neural Network-Based Intelligent Conversation System

Graph and Neural Network-Based Intelligent Conversation System
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Author(s): Anuja Arora (Jaypee Institute of Information Technology, India), Aman Srivastava (Haptik Inc., India) and Shivam Bansal (Exzeo Software Private Limited, India)
Copyright: 2019
Pages: 19
Source title: Nature-Inspired Algorithms for Big Data Frameworks
Source Author(s)/Editor(s): Hema Banati (Dyal Singh College, India), Shikha Mehta (Jaypee Institute of Information Technology, India) and Parmeet Kaur (Jaypee Institute of Information Technology, India)
DOI: 10.4018/978-1-5225-5852-1.ch014

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Abstract

The conventional approach to build a chatbot system uses the sequence of complex algorithms and productivity of these systems depends on order and coherence of algorithms. This research work introduces and showcases a deep learning-based conversation system approach. The proposed approach is an intelligent conversation model approach which conceptually uses graph model and neural conversational model. The proposed deep learning-based conversation system uses neural conversational model over knowledge graph model in a hybrid manner. Graph-based model answers questions written in natural language using its intent in the knowledge graph and neural conversational model converses answer based on conversation content and conversation sequence order. NLP is used in graph model and neural conversational model uses natural language understanding and machine intelligence. The neural conversational model uses seq2seq framework as it requires less feature engineering and lacks domain knowledge. The results achieved through the authors' approach are competitive with solely used graph model results.

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