0.3CLNov 17, 2022
Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State TrackingJihyun Lee, Chaebin Lee, Yunsu Kim et al.
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.