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.
@article{arxiv.2310.11818,
title = {IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization},
author = {Zengguang Hao and Jie Zhang and Binxia Xu and Yafang Wang and Gerard de Melo and Xiaolong Li},
journal= {arXiv preprint arXiv:2310.11818},
year = {2023}
}