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.

翻译：从多轮对话中检测和识别用户意图已成为对话智能体（例如语音助手和智能客服）中广泛探索的技术。传统方法通常将意图挖掘过程视为分类任务。尽管神经网络分类器在此类分类任务中表现出色，但神经网络模型本身的局限性常常阻碍其在实际场景中的部署。我们提出了一种新颖的基于图的多轮对话系统IntentDial，该系统通过强化学习，从动态构建且可扩展的意图图中识别意图要素和标准查询，从而判定用户意图。此外，我们还提供了可视化组件来监控每一轮对话的即时推理路径，这极大地方便了系统的进一步优化。