# SATLab at SemEval-2022 Task 4: Trying to Detect Patronizing and Condescending Language with only Character and Word N-grams

arXiv: https://arxiv.org/abs/2203.05355 · HTML: https://mlsanity.com/paper/2203.05355/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages](https://mlsanity.com/paper/2108.05927/) (2021)
- [TalkDown: A Corpus for Condescension Detection in Context](https://mlsanity.com/paper/1909.11272/) (2019)
- [Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities](https://mlsanity.com/paper/2011.08320/) (2020)
- [A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification](https://mlsanity.com/paper/2202.02511/) (2022)

## Abstract (arXiv)

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.