  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.
