BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection
This addresses the challenge of identifying subtle negative language towards vulnerable communities in NLP, but it is incremental as it applies an existing prompt-based method to a specific task.
The paper tackled the problem of detecting patronizing and condescending language (PCL) in media by using prompt-based learning with DeBERTa, achieving an F1-score of 0.6406 for binary classification and a macro-F1-score of 0.4689 for multi-label classification, ranking first in the SemEval-2022 Task 4 leaderboard.
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.