IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization
This work addresses intent detection for conversational agents like voice assistants and customer services, offering an incremental improvement over existing methods.
The authors tackled the problem of intent detection in multi-turn dialogues by introducing IntentDial, a graph-based system that uses reinforcement learning to identify user intent and provides reasoning path visualization, achieving unspecified performance improvements.
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.