GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection

Junyu Lu, Hao Zhang, Tongyue Zhang, Hongbo Wang, Haohao Zhu, Bo Xu, Hongfei Lin


Abstract
Patronizing and Condescending Language (PCL) towards vulnerable communities in general media has been shown to have potentially harmful effects. Due to its subtlety and the good intentions behind its use, the audience is not aware of the language’s toxicity. In this paper, we present our method for the SemEval-2022 Task4 titled “Patronizing and Condescending Language Detection”. In Subtask A, a binary classification task, we introduce adversarial training based on Fast Gradient Method (FGM) and employ pre-trained model in a unified architecture. For Subtask B, framed as a multi-label classification problem, we utilize various improved multi-label cross-entropy loss functions and analyze the performance of our method. In the final evaluation, our system achieved official rankings of 17/79 and 16/49 on Subtask A and Subtask B, respectively. In addition, we explore the relationship between PCL and emotional polarity and intensity it contains.
Anthology ID:
2022.semeval-1.58
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:
432–437
Language:
URL:
https://aclanthology.org/2022.semeval-1.58
DOI:
10.18653/v1/2022.semeval-1.58
Bibkey:
Cite (ACL):
Junyu Lu, Hao Zhang, Tongyue Zhang, Hongbo Wang, Haohao Zhu, Bo Xu, and Hongfei Lin. 2022. GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 432–437, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection (Lu et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.58.pdf
Video:
 https://aclanthology.org/2022.semeval-1.58.mp4