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Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Maximillian Chen · Alexandros Papangelis · Chenyang Tao · Andy Rosenbaum · Seokhwan Kim · Yang Liu · Zhou Yu · Dilek Hakkani-Tur

Fri Dec 02 07:34 AM -- 07:36 AM (PST) @
in Synthetic Data for Empowering ML Research »
Event URL: https://openreview.net/forum?id=r2_9r7seD-q »

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.

Author Information

Maximillian Chen (Columbia University)

Maximillian Chen is a second-year PhD student at Columbia University. His research has spanned persuasive dialogue systems, low resource techniques for dialogue tasks, and computational social science. Prior to Columbia, he received bachelors degrees in Computer Science and Statistics from Cornell University, where his research focused on computational social science and applied statistics.

Alexandros Papangelis (Amazon)
Chenyang Tao (Amazon)
Andy Rosenbaum (Amazon)
Seokhwan Kim (Amazon Alexa AI)
Yang Liu (Laix)
Zhou Yu (Columbia University)
Dilek Hakkani-Tur (Amazon Alexa AI)

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