Government chatbot: Empowering smart conversations with enhanced contextual understanding and reasoning

Abstract

Currently, an increasing number of governments have adopted question answering systems (QASs) in public service delivery. As some citizens with limited information literacy often express their questions vaguely when interacting with a chatbot, it is necessary to improve the contextual understanding and reasoning ability of government chatbots (G-chatbots). This goal can be achieved through the optimisation of the matching between question, answer and context. By incorporating the Relational Graph Convolutional Networks (R-GCNs) and fuzzy logic, this study proposes a multi-turn dialogue model that introduces a re-question mechanism and a subgraph matching algorithm. The experiment results show that the model can improve the contextual reasoning ability of G-chatbots by about 10% and generate answers in a more explainable way. This study innovatively integrates a question–answer–context matching approach, re-question mechanism into the MTRF-G-chatbot model, reducing barriers to citizens’ access to government services and enhancing contextual reasoning abilities.