  Dialogue understanding tasks often necessitate abundant annotated data to
achieve good performance and that presents challenges in low-resource settings.
To alleviate this barrier, we explore few-shot data augmentation for dialogue
understanding by prompting large pre-trained language models and present a
novel approach that iterates on augmentation quality by applying
weakly-supervised filters. We evaluate our methods on the emotion and act
classification tasks in DailyDialog and the intent classification task in
Facebook Multilingual Task-Oriented Dialogue. Models fine-tuned on our
augmented data mixed with few-shot ground truth data are able to approach or
surpass existing state-of-the-art performance on both datasets. For DailyDialog
specifically, using 10% of the ground truth data we outperform the current
state-of-the-art model which uses 100% of the data.
