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

04/22/2022

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by   Jayant Chhillar, et al.

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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.