SATLab at SemEval-2022 Task 4: Trying to Detect Patronizing and Condescending Language with only Character and Word N-grams
This work addresses the challenge of PCL detection for natural language processing applications, but it is incremental as it applies an existing method to a new task with limited success.
The authors tackled the problem of detecting patronizing and condescending language (PCL) by proposing a logistic regression model using only character and word n-grams, which achieved average performance above a baseline but significantly lower than top teams in SemEval-2022 Task 4.
A logistic regression model only fed with character and word n-grams is proposed for the SemEval-2022 Task 4 on Patronizing and Condescending Language Detection (PCL). It obtained an average level of performance, well above the performance of a system that tries to guess without using any knowledge about the task, but much lower than the best teams. As the proposed model is very similar to the one that performed well on a task requiring to automatically identify hate speech and offensive content, this paper confirms the difficulty of PCL detection.