Controllable User Dialogue Act Augmentation for Dialogue State Tracking

2022/07/26 by Chun-Mao Lai, Lai, Chun-Mao, Ming-Hao Hsu +5 · 1 citation
Computer Science · #Computation and Language (cs.CL) #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Intelligent Tutoring Systems and Adaptive Learning #Speech and dialogue systems

paper · pdf · doi:10.48550/arxiv.2207.12757

openalex publication_date 2022/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only considered the simplest one for augmentation, raising the concern about poor generalization capability. In order to better cover diverse dialogue acts and control the generation quality, this paper proposes controllable user dialogue act augmentation (CUDA-DST) to augment user utterances with diverse behaviors. With the augmented data, different state trackers gain improvement and show better robustness, achieving the state-of-the-art performance on MultiWOZ 2.1

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• ASMR: Augmenting Life Scenario using Large Generative Models for Robotic Action Reflection

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