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

TL;DR

This paper presents a prompt-based approach using pre-trained language models for detecting patronizing and condescending language in media, achieving top leaderboard performance in SemEval-2022 Task 4.

Contribution

The paper introduces a novel prompt-based method with fine-tuned DeBERTa models for implicit language detection, outperforming existing approaches in PCL classification.

Findings

Achieved F1-score of 0.6406 for binary classification

Achieved macro-F1-score of 0.4689 for multi-label classification

Ranked first in the leaderboard for the task

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.