Improving Limited Labeled Dialogue State Tracking with Self-Supervision
Chien-Sheng Wu, Steven C. H. Hoi, Caiming Xiong · 2020
Existing dialogue state tracking (DST) models require plenty of labeled data.However, collecting high-quality labels is costly, especially when the number of domains increases.In this paper, we address a practical DST problem that is rarely discussed, i.e., learning efficiently with limited labeled data.We present and investigate two self-supervised objectives: preserving latent consistency and modeling conversational behavior.We encourage a DST model to have consistent latent distributions given a perturbed input, making it more robust to an unseen scenario.We also add an auxiliary utterance generation task, modeling a potential correlation between conversational behavior and dialogue states.The experimental results show that our proposed self-supervised signals can improve joint goal accuracy by 8.95% when only 1% labeled data is used on the MultiWOZ dataset.We can achieve an additional 1.76% improvement if some unlabeled data is jointly trained as semi-supervised learning.We analyze and visualize how our proposed self-supervised signals help the DST task and hope to stimulate future data-efficient DST research.