UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language
David Koleczek, Alexander Scarlatos, Preshma Linet Pereira, Siddha Makarand Karkare
Abstract
Patronizing and condescending language (PCL) is everywhere, but rarely is the focus on its use by media towards vulnerable communities. Accurately detecting PCL of this form is a difficult task due to limited labeled data and how subtle it can be. In this paper, we describe our system for detecting such language which was submitted to SemEval 2022 Task 4: Patronizing and Condescending Language Detection. Our approach uses an ensemble of pre-trained language models, data augmentation, and optimizing the threshold for detection. Experimental results on the evaluation dataset released by the competition hosts show that our work is reliably able to detect PCL, achieving an F1 score of 55.47% on the binary classification task and a macro F1 score of 36.25% on the fine-grained, multi-label detection task.- Anthology ID:
- 2022.semeval-1.60
- 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:
- 445–453
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.60
- DOI:
- 10.18653/v1/2022.semeval-1.60
- Cite (ACL):
- David Koleczek, Alexander Scarlatos, Preshma Linet Pereira, and Siddha Makarand Karkare. 2022. UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 445–453, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language (Koleczek et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.semeval-1.60.pdf
- Data
- BiasCorp, SBIC