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

• URL: http://arxiv.org/abs/2305.02459v2
• Date: Fri, 5 May 2023 00:56:19 GMT
• Title: Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge
• Authors: Vasudha Varadarajan, Swanie Juhng, Syeda Mahwish, Xiaoran Liu, Jonah Luby, Christian Luhmann, H. Andrew Schwartz
• Abstract summary: We propose and investigate transfer- and active learning solutions to the rare class problem of dissonance detection. We perform experiments for a specific rare class problem: collecting language samples of cognitive dissonance from social media. We find that probability-of-rare-class (PRC) is a simple and effective strategy to guide annotations and ultimately improve model accuracy.
• Score: 7.61140479230184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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