@inproceedings{varadarajan-etal-2023-transfer,
title = "Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge",
author = "Varadarajan, Vasudha and
Juhng, Swanie and
Mahwish, Syeda and
Liu, Xiaoran and
Luby, Jonah and
Luhmann, Christian and
Schwartz, H. Andrew",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.665",
doi = "10.18653/v1/2023.acl-long.665",
pages = "11923--11936",
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. {\textless} 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.",
}
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<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. \textless 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.</abstract>
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%0 Conference Proceedings
%T Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge
%A Varadarajan, Vasudha
%A Juhng, Swanie
%A Mahwish, Syeda
%A Liu, Xiaoran
%A Luby, Jonah
%A Luhmann, Christian
%A Schwartz, H. Andrew
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F varadarajan-etal-2023-transfer
%X 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. \textless 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.
%R 10.18653/v1/2023.acl-long.665
%U https://aclanthology.org/2023.acl-long.665
%U https://doi.org/10.18653/v1/2023.acl-long.665
%P 11923-11936
Markdown (Informal)
[Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge](https://aclanthology.org/2023.acl-long.665) (Varadarajan et al., ACL 2023)
ACL