Intent detection and identification from multi-turn dialogue has become a
widely explored technique in conversational agents, for example, voice
assistants and intelligent customer services. The conventional approaches
typically cast the intent mining process as a classification task. Although
neural classifiers have proven adept at such classification tasks, the issue of
neural network models often impedes their practical deployment in real-world
settings. We present a novel graph-based multi-turn dialogue system called ,
which identifies a user's intent by identifying intent elements and a standard
query from a dynamically constructed and extensible intent graph using
reinforcement learning. In addition, we provide visualization components to
monitor the immediate reasoning path for each turn of a dialogue, which greatly
facilitates further improvement of the system.Comment: 4pages, 5 figure