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Patronizing and condescending language (PCL) is everywhere, but rarely is thefocus on its use by media towards vulnerable communities. Accurately detectingPCL of this form is a difficult task due to limited labeled data and how subtleit can be. In this paper, we describe our system for detecting such languagewhich was submitted to SemEval 2022 Task 4: Patronizing and CondescendingLanguage Detection. Our approach uses an ensemble of pre-trained languagemodels, data augmentation, and optimizing the threshold for detection.Experimental results on the evaluation dataset released by the competitionhosts show that our work is reliably able to detect PCL, achieving an F1 scoreof 55.47% on the binary classification task and a macro F1 score of 36.25% onthe fine-grained, multi-label detection task.

UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language