Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State Tracking. (arXiv:2211.09379v1 [cs.CL])

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

arxiv augmentation self-training state tracking training