Task-oriented dialog (TOD) systems typically manage structured knowledge (e.g. ontologies and databases) to guide the goal-oriented conversations. However, they fall short of handling dialog turns grounded on unstructured knowledge (e.g. reviews and documents). In this paper, we formulate a task of modeling TOD grounded on both structured and unstructured knowledge. To address this task, we propose a TOD system with hybrid knowledge management, HyKnow. It extends the belief state to manage both structured and unstructured knowledge, and is the first end-to-end model that jointly optimizes dialog modeling grounded on these two kinds of knowledge. We conduct experiments on the modified version of MultiWOZ 2.1 dataset, where dialogs are grounded on hybrid knowledge. Experimental results show that HyKnow has strong end-to-end performance compared to existing TOD systems. It also outperforms the pipeline knowledge management schemes, with higher unstructured knowledge retrieval accuracy.
翻译:以任务为导向的对话(TOD)系统通常管理结构化知识(例如,关于主题和数据库),以指导面向目标的对话,但是,它们还不能满足基于非结构化知识(例如,审查和文件)的处理对话。在本文件中,我们根据结构化和无结构化知识,制定了一个模拟TOD的任务。为了完成这项任务,我们提议了一个具有混合知识管理的TOD系统HyKnow。它扩大了信仰状态,以管理结构化和非结构化知识,并且是第一个在这两种知识的基础上共同优化对话模式的端到端模式。我们实验了多WOZ 2.1数据集的修改版本,其中对话以混合知识为基础。实验结果显示HyKind与现有的TOD系统相比,终端到终端的绩效很强。它也超过了编审知识管理计划,而没有结构化的知识检索准确性更高。