Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, but ignore 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 large language models in zero-shot scenarios, improving average joint goal accuracy by $8\%$ across all domains in MultiWOZ.

翻译：以往的零样本对话状态跟踪（DST）方法仅应用迁移学习，但忽略了目标领域中的未标注数据。我们通过联合训练和自训练方法利用此类未标注数据，将零样本DST转化为少样本DST。我们的方法融合了辅助任务，这些任务生成作为主任务逆提示的槽类型，并在联合训练过程中创建槽值。这两项任务之间的循环一致性能够在未知目标领域中生成并筛选高质量样本，以供后续微调使用。该方法还促进了自动标签创建，从而优化了DST模型的训练和微调过程。我们在零样本场景下的大型语言模型上验证了该方法的有效性，在MultiWOZ数据集的所有领域中平均联合目标准确率提升了8%。