In previous zero-shot DST, transferring learning methods are adopted while the unlabelled data in the target domain is ignored. We leverage the unlabelled data in the zero-shot DST by transforming the zero-shot problem into a few-shot problem with a two-step training strategy. Our proposed methods outperform the baseline by 8%