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

2022-04-22 06:11:47
Jayant Chhillar
arXiv_CL
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2204.10519

PDF

https://arxiv.org/pdf/2204.10519.pdf

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