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
- 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)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.semeval-1.68.pdf