SemEval-2022 Task 4: Patronizing and Condescending Language Detection
This paper presents an overview of Task 4 at SemEval-2022, which was focused on detecting Patronizing and Condescending Language (PCL) towards vulnerable communities. Two sub-tasks were considered: a binary classification task, where participants needed to classify a given paragraph as containing PCL or not, and a multi-label classification task, where participants needed to identify which types of PCL are present (if any). The task attracted more than 300 participants, 77 teams and 229 valid submissions. We provide an overview of how the task was organized, discuss the techniques that were employed by the different participants, and summarize the main resulting insights about PCL detection and categorization.
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Datasets
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DPMResults from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Binary Condescension Detection | DPM | RoBERTa Baseline | F1-score | 49.1 | # 5 | |
| Multi-label Condescension Detection | DPM | RoBERTa Baseline | Macro-F1 | 10.4 | # 5 | |
| SemEval-2022 Task 4-2 (Multi-label PCL Detection) | DPM | RoBERTa Baseline | Macro-F1 | 10.4 | # 1 | |
| SemEval-2022 Task 4-1 (Binary PCL Detection) | DPM | RoBERTa Baseline | F1-score | 49.1 | # 1 |
