Knowledge-grounded Dialog State Tracking

• URL: http://arxiv.org/abs/2210.06656v1
• Date: Thu, 13 Oct 2022 01:34:08 GMT
• Title: Knowledge-grounded Dialog State Tracking
• Authors: Dian Yu, Mingqiu Wang, Yuan Cao, Izhak Shafran, Laurent El Shafey, Hagen Soltau
• Abstract summary: We propose to perform dialog state tracking grounded on knowledge encoded externally. We query relevant knowledge of various forms based on the dialog context. We demonstrate superior performance of our proposed method over strong baselines.
• Score: 12.585986197627477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge (including structured knowledge such as schema and ontology, and unstructured knowledge such as web corpus) is a critical part of dialog understanding, especially for unseen tasks and domains. Traditionally, such domain-specific knowledge is encoded implicitly into model parameters for the execution of downstream tasks, which makes training inefficient. In addition, such models are not easily transferable to new tasks with different schemas. In this work, we propose to perform dialog state tracking grounded on knowledge encoded externally. We query relevant knowledge of various forms based on the dialog context where such information can ground the prediction of dialog states. We demonstrate superior performance of our proposed method over strong baselines, especially in the few-shot learning setting.

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