Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language. (arXiv:2204.10519v1 [cs.CL])

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.

arxiv language roberta