Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge

Vasudha Varadarajan, Swanie Juhng, Syeda Mahwish, Xiaoran Liu, Jonah Luby, Christian Luhmann, H. Andrew Schwartz


Abstract
While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks – when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selection strategy, the criteria by which rare-class examples are chosen, has not been systematically evaluated. Further, transformers enable iterative transfer-learning approaches. We propose and investigate transfer- and active learning solutions to the rare class problem of dissonance detection through utilizing models trained on closely related tasks and the evaluation of acquisition strategies, including a proposed probability-of-rare-class (PRC) approach. We perform these experiments for a specific rare-class problem: collecting language samples of cognitive dissonance from social media. We find that PRC is a simple and effective strategy to guide annotations and ultimately improve model accuracy while transfer-learning in a specific order can improve the cold-start performance of the learner but does not benefit iterations of active learning.
Anthology ID:
2023.acl-long.665
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11923–11936
Language:
URL:
https://aclanthology.org/2023.acl-long.665
DOI:
10.18653/v1/2023.acl-long.665
Award:
 Outstanding Paper Award
Bibkey:
Cite (ACL):
Vasudha Varadarajan, Swanie Juhng, Syeda Mahwish, Xiaoran Liu, Jonah Luby, Christian Luhmann, and H. Andrew Schwartz. 2023. Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11923–11936, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge (Varadarajan et al., ACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.acl-long.665.pdf
Video:
 https://aclanthology.org/2023.acl-long.665.mp4