End-to-End Task-Oriented Dialog Modeling with Semi-Structured Knowledge Management
This addresses a limitation in task-oriented dialog systems for applications requiring mixed knowledge types, but it is incremental as it builds on existing methods.
The paper tackles the problem of task-oriented dialog systems handling both structured and unstructured knowledge by proposing SeKnow, a system that extends belief states to manage semi-structured knowledge, and shows strong performance on a modified MultiWOZ 2.1 dataset.
Current task-oriented dialog (TOD) systems mostly manage structured knowledge (e.g. databases and tables) to guide the goal-oriented conversations. However, they fall short of handling dialogs which also involve unstructured knowledge (e.g. reviews and documents). In this paper, we formulate a task of modeling TOD grounded on a fusion of structured and unstructured knowledge. To address this task, we propose a TOD system with semi-structured knowledge management, SeKnow, which extends the belief state to manage knowledge with both structured and unstructured contents. Furthermore, we introduce two implementations of SeKnow based on a non-pretrained sequence-to-sequence model and a pretrained language model, respectively. Both implementations use the end-to-end manner to jointly optimize dialog modeling grounded on structured and unstructured knowledge. We conduct experiments on a modified version of MultiWOZ 2.1 dataset, Mod-MultiWOZ 2.1, where dialogs are processed to involve semi-structured knowledge. Experimental results show that SeKnow has strong performances in both end-to-end dialog and intermediate knowledge management, compared to existing TOD systems and their extensions with pipeline knowledge management schemes.