Abstract
This paper introduces the related work and the results of Team Sapphire’s system for SemEval-2022 Task 4: Patronizing and Condescending Language Detection. We only participated in subtask 1. The task goal is to judge whether a news text contains PCL. This task can be considered as a task of binary classification of news texts. In this binary classification task, the BERT-base model is adopted as the pre-trained model used to represent textual information in vector form and encode it. Capsule networks is adopted to extract features from the encoded vectors. The official evaluation metric for subtask 1 is the F1 score over the positive class. Finally, our system’s submitted prediction results on test set achieved the score of 0.5187.- Anthology ID:
- 2022.semeval-1.54
- 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:
- 405–408
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.54
- DOI:
- 10.18653/v1/2022.semeval-1.54
- Cite (ACL):
- Sihui Li and Xiaobing Zhou. 2022. Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 405–408, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks (Li & Zhou, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.semeval-1.54.pdf