2000 character limit reached
Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language (2204.10519v1)
Published 22 Apr 2022 in cs.CL and cs.LG
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 LLM 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.