Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State Tracking
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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 approximately 4 values compared to baseline.
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