HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management
This addresses a limitation in task-oriented dialog systems for applications requiring integration of diverse knowledge sources, representing an incremental improvement by extending belief state management.
The paper tackles the problem of task-oriented dialog systems handling both structured and unstructured knowledge, proposing HyKnow as an end-to-end model that jointly optimizes dialog modeling on hybrid knowledge, achieving strong performance and higher retrieval accuracy compared to existing systems.
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.