  Data augmentation methods have been a promising direction to improve the
performance of small models for low-resource dialogue state tracking. However,
traditional methods rely on pre-defined user goals and neglect the importance
of data complexity in this task. In this paper, we propose EDZ-DA, an
Easy-to-Difficult Zero-shot Data Augmentation framework for low-resource
dialogue state tracking that utilizes large language models to automatically
catch the relationships of different domains and then generate the dialogue
data. We also complicate the dialogues based on the domain relation to enhance
the model's capability for co-reference slot tracking. Furthermore, we permute
slot values to mitigate the influence of output orders and the problem of
incomplete value generation. Experimental results illustrate the superiority of
our proposed method compared to previous strong data augmentation baselines on
MultiWOZ.
