Patronizing and Condescending Language Detection.

Patronizing and Condescending Language (PCL) is the language used by an individual that denotes a superior attitude toward other individuals, especially those that are members of minority or marginalized groups. Due to the prevalence of patronizing and condescending language in today's society, there has been a focus on attempting to automate its detection and classification. In this study, we develop classifiers for this problem using the data from the previously concluded “SemEval 2022 Task 4: Patronizing and Condescending Language Detection” competition. We explore the implementation of three traditional machine learning algorithms and three transformer-based algorithms for both binary and multi-label PCL classification. Our results are in line with that of the original SemEval findings and help demonstrate the need for additional work. The development of a larger, more-balanced dataset would ensure more consistent and transferable results.