BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection

• URL: http://arxiv.org/abs/2208.01312v1
• Date: Tue, 2 Aug 2022 08:38:47 GMT
• Title: BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection
• Authors: Yong Deng, Chenxiao Dou, Liangyu Chen, Deqiang Miao, Xianghui Sun, Baochang Ma, Xiangang Li
• Abstract summary: PCL detection task is aimed at identifying language that is patronizing or condescending towards vulnerable communities in the general media. In this paper, we give an introduction to our solution, which exploits the power of prompt-based learning on paragraph classification.
• Score: 13.944149742291788
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media.Compared to other NLP tasks of paragraph classification, the negative language presented in the PCL detection task is usually more implicit and subtle to be recognized, making the performance of common text-classification approaches disappointed. Targeting the PCL detection problem in SemEval-2022 Task 4, in this paper, we give an introduction to our team's solution, which exploits the power of prompt-based learning on paragraph classification. We reformulate the task as an appropriate cloze prompt and use pre-trained Masked Language Models to fill the cloze slot. For the two subtasks, binary classification and multi-label classification, DeBERTa model is adopted and fine-tuned to predict masked label words of task-specific prompts. On the evaluation dataset, for binary classification, our approach achieves an F1-score of 0.6406; for multi-label classification, our approach achieves an macro-F1-score of 0.4689 and ranks first in the leaderboard.

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