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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 modelingTOD grounded on a fusion of structured and unstructured knowledge. To addressthis task, we propose a TOD system with semi-structured knowledge management,SeKnow, which extends the belief state to manage knowledge with both structuredand unstructured contents. Furthermore, we introduce two implementations ofSeKnow based on a non-pretrained sequence-to-sequence model and a pretrainedlanguage model, respectively. Both implementations use the end-to-end manner tojointly optimize dialog modeling grounded on structured and unstructuredknowledge. We conduct experiments on a modified version of MultiWOZ 2.1dataset, Mod-MultiWOZ 2.1, where dialogs are processed to involvesemi-structured knowledge. Experimental results show that SeKnow has strongperformances in both end-to-end dialog and intermediate knowledge management,compared to existing TOD systems and their extensions with pipeline knowledgemanagement schemes.

End-to-End Task-Oriented Dialog Modeling with Semi-Structured Knowledge Management