Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
This work addresses data scarcity in dialogue understanding for low-resource settings, offering a practical solution that is incremental but effective in specific domains.
The paper tackles the problem of limited annotated data for dialogue understanding by proposing a few-shot data augmentation method using prompting and weakly-supervised filters, achieving state-of-the-art performance on tasks like emotion and act classification in DailyDialog and intent classification in Facebook Multilingual Task-Oriented Dialogue, with results showing that using only 10% of ground truth data can outperform models using 100%.
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.