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ACL Findings2021

HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management

Silin Gao, Ryuichi Takanobu, Wei Peng, Qun Liu, Minlie Huang

Abstract

Task-oriented dialog (TOD) systems typically manage structured knowledge (e.g. ontologies and databases) to guide the goal-oriented conversations. However, they fall short of handling dialog turns grounded on unstructured knowledge (e.g. reviews and documents). In this paper, we formulate a task of modeling TOD grounded on both structured and unstructured knowledge. To address this task, we propose a TOD system with hybrid knowledge management, HyKnow. It extends the belief state to manage both structured and unstructured knowledge, and is the first end-to-end model that jointly optimizes dialog modeling grounded on these two kinds of knowledge. We conduct experiments on the modified version of MultiWOZ 2.1 dataset, where dialogs are grounded on hybrid knowledge. Experimental results show that HyKnow has strong end-to-end performance compared to existing TOD systems. It also outperforms the pipeline knowledge management schemes, with higher unstructured knowledge retrieval accuracy.

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Builds on8

  1. Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue DatasetAbhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta et al.AAAI 2020 · 707 citations
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  3. Task-Oriented Dialog Systems That Consider Multiple Appropriate Responses under the Same ContextYichi Zhang, Zhijian Ou, Zhou YuAAAI 2020 · 198 citations
  4. doc2dial: A Goal-Oriented Document-Grounded Dialogue DatasetSong Feng, Hui Wan, R. Chulaka Gunasekara, Siva Sankalp Patel et al.EMNLP 2020 · 87 citations
  5. MOSS: End-to-End Dialog System Framework with Modular SupervisionWeixin Liang, Youzhi Tian, Chengcai Chen, Zhou YuAAAI 2020 · 55 citations

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