Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Abstract
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.
Cite
@article{arxiv.2210.14169,
title = {Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding},
author = {Maximillian Chen and Alexandros Papangelis and Chenyang Tao and Andy Rosenbaum and Seokhwan Kim and Yang Liu and Zhou Yu and Dilek Hakkani-Tur},
journal= {arXiv preprint arXiv:2210.14169},
year = {2022}
}
Comments
To appear in SyntheticData4ML @ NeurIPS 2022. 16 pages, 10 figures, 3 tables