Improving Longer-range Dialogue State Tracking

• URL: http://arxiv.org/abs/2103.00109v1
• Date: Sat, 27 Feb 2021 02:44:28 GMT
• Title: Improving Longer-range Dialogue State Tracking
• Authors: Ye Zhang, Yuan Cao, Mahdis Mahdieh, Jefferey Zhao, Yonghui Wu
• Abstract summary: Dialogue state tracking (DST) is a pivotal component in task-oriented dialogue systems. In this paper, we aim to improve the overall performance of DST with a special focus on handling longer dialogues.
• Score: 22.606650177804966
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dialogue state tracking (DST) is a pivotal component in task-oriented dialogue systems. While it is relatively easy for a DST model to capture belief states in short conversations, the task of DST becomes more challenging as the length of a dialogue increases due to the injection of more distracting contexts. In this paper, we aim to improve the overall performance of DST with a special focus on handling longer dialogues. We tackle this problem from three perspectives: 1) A model designed to enable hierarchical slot status prediction; 2) Balanced training procedure for generic and task-specific language understanding; 3) Data perturbation which enhances the model's ability in handling longer conversations. We conduct experiments on the MultiWOZ benchmark, and demonstrate the effectiveness of each component via a set of ablation tests, especially on longer conversations.

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