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