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

Jayant Chhillar


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 15th rank with an F1-score of 0.5924 for subtask-A and 12th in subtask-B with a macro-F1 score of 0.3763.
Anthology ID:
2022.semeval-1.68
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
496–502
Language:
URL:
https://aclanthology.org/2022.semeval-1.68
DOI:
10.18653/v1/2022.semeval-1.68
Bibkey:
Cite (ACL):
Jayant Chhillar. 2022. Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 496–502, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language (Chhillar, SemEval 2022)
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PDF:
https://aclanthology.org/2022.semeval-1.68.pdf