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

Jihyun Lee; Chaebin Lee; Yunsu Kim; Gary Geunbae Lee

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arXiv · 2211.09379

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

Jihyun Lee·Chaebin Lee·Yunsu Kim·Gary Geunbae Lee

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Abstract

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.

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Jihyun Lee, Chaebin Lee, Yunsu Kim, Gary Geunbae Lee. 2022-11-17. Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State Tracking. https://arxiv.org/abs/2211.09379

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