Leveraging Semi-Supervised Learning and Generative Adversarial Networks with Transformer and Flair Embeddings for Detecting Patronizing and Condescending Language

This study addresses the intricate challenge of detecting Patronizing and Condescending Language (PCL), a subtle yet impactful form of discriminatory communication often targeting vulnerable populations. PCL detection necessitates a nuanced understanding of human values and commonsense reasoning, making it difficult for both human judges and NLP systems. The SemEval-2022 Task 4 proposed two subtasks: identifying PCL and classifying its categories, highlighting the complexity of this issue. We propose an advanced framework combining transformer-based embeddings, Flair embeddings, and document pool embeddings to effectively identify and classify PCL. This integration enables the framework to capture both contextual and categorical features, addressing the implicit and nuanced nature of PCL. Additionally, fine-tuning the proposed model enhances its ability to discern subtle linguistic patterns, improving prediction accuracy. To validate the robustness of the approach, ablation studies and comparative experiments against other transformer- based models were conducted, demonstrating its efficiency.