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

• URL: http://arxiv.org/abs/2211.09379v1
• Date: Thu, 17 Nov 2022 07:13:58 GMT
• Title: Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State Tracking
• Authors: Jihyun Lee, Chaebin Lee, Yunsu Kim, Gary Geunbae Lee
• Abstract summary: 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.
• Score: 14.709084509818474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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