Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis

arXiv:2310.00347v3 Announce Type: replace
Abstract: Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challenges in generalizing beyond their training data and are typically designed for a single task, often focusing on bias detection at the sentence level. To address this, we present the Contextualized Bi-Directional Dual Transformer (CBDT) \textcolor{green}{\faLeaf} classifier. This model combines two complementary transformer networks: the Context Transformer and the Entity Transformer, with a …

abstract analysis arxiv beyond bias challenges cs.ai cs.cl data decision detection face language language models making misinformation negative stereotypes text training training data transformer transformer-based models type