PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection
Dou Hu, Zhou Mengyuan, Xiyang Du, Mengfei Yuan, Jin Zhi, Lianxin Jiang, Mo Yang, Xiaofeng Shi
Abstract
Patronizing and condescending language (PCL) has a large harmful impact and is difficult to detect, both for human judges and existing NLP systems. At SemEval-2022 Task 4, we propose a novel Transformer-based model and its ensembles to accurately understand such language context for PCL detection. To facilitate comprehension of the subtle and subjective nature of PCL, two fine-tuning strategies are applied to capture discriminative features from diverse linguistic behaviour and categorical distribution. The system achieves remarkable results on the official ranking, including 1st in Subtask 1 and 5th in Subtask 2. Extensive experiments on the task demonstrate the effectiveness of our system and its strategies.- Anthology ID:
- 2022.semeval-1.43
- 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:
- 335–343
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.43
- DOI:
- 10.18653/v1/2022.semeval-1.43
- Cite (ACL):
- Dou Hu, Zhou Mengyuan, Xiyang Du, Mengfei Yuan, Jin Zhi, Lianxin Jiang, Mo Yang, and Xiaofeng Shi. 2022. PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 335–343, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection (Hu et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.semeval-1.43.pdf
- Data
- DPM