Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language

Abstract

This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4). This work explores different models based on the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The best models achieved 15$^{th}$ rank with an F1-score of 0.5924 for subtask-A and 12$^{th}$ in subtask-B with a macro-F1 score of 0.3763.