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.
Cite
@article{arxiv.2211.09379,
title = {Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State Tracking},
author = {Jihyun Lee and Chaebin Lee and Yunsu Kim and Gary Geunbae Lee},
journal= {arXiv preprint arXiv:2211.09379},
year = {2022}
}