Abstract
An understanding of patronizing and condescending language detection is an important part of identifying and addressing discrimination and prejudice in various forms of communication. In this paper, we investigate several methods for detecting patronizing and condescending language in short statements as part of SemEval-2022 Task 4. For Task 1a, we investigate applying both lightweight (tree-based and linear) machine learning classification models and fine-tuned pre-trained large language models. Our final system achieves an F1-score of 0.4321, recall-score of 0.5016, and a precision-score of 0.3795 (ranked 53 / 78) on Task 1a.- Anthology ID:
- 2022.semeval-1.49
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 374–378
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.49
- DOI:
- 10.18653/v1/2022.semeval-1.49
- Cite (ACL):
- Rylan Yang, Ethan Chi, and Nathan Chi. 2022. RNRE-NLP at SemEval-2022 Task 4: Patronizing and Condescending Language Detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 374–378, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- RNRE-NLP at SemEval-2022 Task 4: Patronizing and Condescending Language Detection (Yang et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.semeval-1.49.pdf