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In dialogue state tracking (DST), labeling the dataset involves considerablehuman labor. We propose a new self-training framework for few-shot generativeDST that utilize unlabeled data. Our self-training method iteratively improvesthe model by pseudo labeling and employs Purpose Preserving Augmentation(PPAug) to prevent overfitting. We increaese the few-shot 10% performance byapproximately 4% on MultiWOZ 2.1 and enhances the slot-recall 8.34% for unseenvalues compared to baseline.

Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State Tracking