A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without using any supervised data, zero-shot domain adaptation. Parameter-Efficient Transfer Learning (PETL) has the potential to address this problem due to its robustness. However, it has yet to be applied to the zero-shot scenarios, as it is not clear how to apply it unsupervisedly. Our method, Prompter, uses descriptions of target domain slots to generate dynamic prefixes that are concatenated to the key and values at each layer's self-attention mechanism. This allows for the use of prefix-tuning in zero-shot. Prompter outperforms previous methods on both the MultiWOZ and SGD benchmarks. In generating prefixes, our analyses find that Prompter not only utilizes the semantics of slot descriptions but also how often the slots appear together in conversation. Moreover, Prompter's gains are due to its improved ability to distinguish "none"-valued dialogue slots, compared against baselines.

翻译：对话状态跟踪（DST）领域面临的一个挑战是在不使用任何监督数据的情况下将模型适应到新领域，即零样本领域自适应。参数高效迁移学习（PETL）因其鲁棒性而具有解决该问题的潜力。然而，目前尚不清楚如何将其无监督地应用于零样本场景，因此该技术尚未被应用于此类问题。我们的方法Prompter利用目标领域槽位的描述生成动态前缀，这些前缀被拼接至每层自注意力机制中的键和值上，从而实现了零样本场景下的前缀微调。Prompter在MultiWOZ和SGD两个基准测试中均优于先前方法。我们的分析发现，在生成前缀时，Prompter不仅利用了槽位描述的语义信息，还考虑了对话中槽位共现的频率。此外，相对于基线方法，Prompter的性能提升源于其增强的区分“无值”对话槽位的能力。