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

2022/04/22 by Jayant Chhillar, Chhillar, Jayant
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.CL #cs.LG

paper · pdf · doi:10.48550/arxiv.2204.10519

Accepted at SemEval-2022, 7 pages, 6 Figures, 7 Tables

arxiv created 2022/04/22 · arxiv updated 2022/04/25

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.

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