Zero-shot Generalization in Dialog State Tracking through Generative Question Answering

• URL: http://arxiv.org/abs/2101.08333v1
• Date: Wed, 20 Jan 2021 21:47:20 GMT
• Title: Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
• Authors: Shuyang Li, Jin Cao, Mukund Sridhar, Henghui Zhu, Shang-Wen Li, Wael Hamza, Julian McAuley
• Abstract summary: We introduce a novel framework that supports natural language queries for unseen constraints and slots in task-oriented dialogs. Our approach is based on generative question-answering using a conditional domain model pre-trained on substantive English sentences.
• Score: 10.81203437307028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in task-oriented dialogs. In real-world settings with constantly changing services, DST systems must generalize to new domains and unseen slot types. Existing methods for DST do not generalize well to new slot names and many require known ontologies of slot types and values for inference. We introduce a novel ontology-free framework that supports natural language queries for unseen constraints and slots in multi-domain task-oriented dialogs. Our approach is based on generative question-answering using a conditional language model pre-trained on substantive English sentences. Our model improves joint goal accuracy in zero-shot domain adaptation settings by up to 9% (absolute) over the previous state-of-the-art on the MultiWOZ 2.1 dataset.

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