A Domain-adaptive Pre-training Approach for Language Bias Detection in News
Media bias is a multi-faceted construct influencing individual behavior and collective decision-making. Slanted news reporting is the result of one-sided and polarized writing which can occur in various forms. In this work, we focus on an important form of media bias, i.e. bias by word choice. Detecting biased word choices is a challenging task due to its linguistic complexity and the lack of representative gold-standard corpora. We present DA-RoBERTa, a new state-of-the-art transformer-based model adapted to the media bias domain which identifies sentence-level bias with an F1 score of 0.814. In addition, we also train, DA-BERT and DA-BART, two more transformer models adapted to the bias domain. Our proposed domain-adapted models outperform prior bias detection approaches on the same data.
Code (1)
Tasks
Bias DetectionDecision MakingSentenceSimilar Papers 제목 키워드 기반
Learning Domain-Aware Detection Head with Prompt Tuning
Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. However, existing methods focus on reducing the domain bias of the detection ba…
CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection
Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using the semi-supervised student-teacher fram…
Domain Adaptationobject-detectionObject DetectionExplaining News Bias Detection: A Comparative SHAP Analysis of Transformer Model Decision Mechanisms
Automated bias detection in news text is heavily used to support journalistic analysis and media accountability, yet little is known about how bias detection models arrive at their decisions or why they fail. In this wor…
Bias DetectionDA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection
Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. As the visual-language models (VLMs) can provide essential general knowledge on u…
General Knowledgeobject-detectionObject DetectionFIT: Frequency-based Image Translation for Domain Adaptive Object Detection
Domain adaptive object detection (DAOD) aims to adapt the detector from a labelled source domain to an unlabelled target domain. In recent years, DAOD has attracted massive attention since it can alleviate performance de…
object-detectionObject DetectionTranslation