@inproceedings{hu-etal-2022-pali,
title = "{PALI}-{NLP} at {S}em{E}val-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection",
author = "Hu, Dou and
Mengyuan, Zhou and
Du, Xiyang and
Yuan, Mengfei and
Zhi, Jin and
Jiang, Lianxin and
Yang, Mo and
Shi, Xiaofeng",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.43",
doi = "10.18653/v1/2022.semeval-1.43",
pages = "335--343",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection
%A Hu, Dou
%A Mengyuan, Zhou
%A Du, Xiyang
%A Yuan, Mengfei
%A Zhi, Jin
%A Jiang, Lianxin
%A Yang, Mo
%A Shi, Xiaofeng
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F hu-etal-2022-pali
%X 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.
%R 10.18653/v1/2022.semeval-1.43
%U https://aclanthology.org/2022.semeval-1.43
%U https://doi.org/10.18653/v1/2022.semeval-1.43
%P 335-343
Markdown (Informal)
[PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection](https://aclanthology.org/2022.semeval-1.43) (Hu et al., SemEval 2022)
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.