Zero-shot dialogue state tracking (DST) transfers knowledge to unseen domains, reducing the cost of annotating new datasets. Previous zero-shot DST models mainly suffer from domain transferring and partial prediction problems. To address these challenges, we propose Mixture of Prefix Experts (MoPE) to establish connections between similar slots in different domains, which strengthens the model transfer performance in unseen domains. Empirical results demonstrate that MoPE-DST achieves the joint goal accuracy of 57.13% on MultiWOZ2.1 and 55.40% on SGD.

翻译：零样本对话状态跟踪（DST）将知识迁移至未见领域，从而降低新数据集标注的成本。以往零样本DST模型主要面临领域迁移与部分预测问题。为应对这些挑战，我们提出混合前缀专家（MoPE），在不同领域的相似槽位间建立关联，从而增强模型在未见领域中的迁移性能。实验结果表明，MoPE-DST在MultiWOZ2.1上联合目标准确率达到57.13%，在SGD上达到55.40%。