  Previous zero-shot dialogue state tracking (DST) methods only apply transfer
learning, ignoring unlabelled data in the target domain. We transform zero-shot
DST into few-shot DST by utilising such unlabelled data via joint and
self-training methods. Our method incorporates auxiliary tasks that generate
slot types as inverse prompts for main tasks, creating slot values during joint
training. Cycle consistency between these two tasks enables the generation and
selection of quality samples in unknown target domains for subsequent
fine-tuning. This approach also facilitates automatic label creation, thereby
optimizing the training and fine-tuning of DST models. We demonstrate this
method's effectiveness on general language models in zero-shot scenarios,
improving average joint goal accuracy by 8% across all domains in MultiWOZ.
