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Intent detection and identification from multi-turn dialogue has become awidely explored technique in conversational agents, for example, voiceassistants and intelligent customer services. The conventional approachestypically cast the intent mining process as a classification task. Althoughneural classifiers have proven adept at such classification tasks, the issue ofneural network models often impedes their practical deployment in real-worldsettings. We present a novel graph-based multi-turn dialogue system called ,which identifies a user's intent by identifying intent elements and a standardquery from a dynamically constructed and extensible intent graph usingreinforcement learning. In addition, we provide visualization components tomonitor the immediate reasoning path for each turn of a dialogue, which greatlyfacilitates further improvement of the system.

IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization