Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking

• URL: http://arxiv.org/abs/2105.04222v1
• Date: Mon, 10 May 2021 09:34:01 GMT
• Title: Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking
• Authors: Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba
• Abstract summary: Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data. We propose a slot description enhanced generative approach for zero-shot cross-domain DST.
• Score: 50.04597636485369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data. In this paper, we propose a slot description enhanced generative approach for zero-shot cross-domain DST. Specifically, our model first encodes dialogue context and slots with a pre-trained self-attentive encoder, and generates slot values in an auto-regressive manner. In addition, we incorporate Slot Type Informed Descriptions that capture the shared information across slots to facilitate cross-domain knowledge transfer. Experimental results on the MultiWOZ dataset show that our proposed method significantly improves existing state-of-the-art results in the zero-shot cross-domain setting.

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