From Fake to Hyperpartisan News Detection Using Domain Adaptation
Răzvan-Alexandru Smădu Sebastian-Vasile Echim Dumitru-Clementin Cercel Iuliana Marin Florin Pop
Unsupervised Domain Adaptation (UDA) is a popular technique that aims to
reduce the domain shift between two data distributions. It was successfully
applied in computer vision and natural language processing. In the current
work, we explore the effects of various unsupervised domain adaptation
techniques between two text classification tasks: fake and hyperpartisan news
detection. We investigate the knowledge transfer from fake to hyperpartisan
news detection without involving target labels during training. Thus, we
evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive
learning. Extensive experiments show that these techniques improve performance,
while including data augmentation further enhances the results. In addition, we
combine clustering and topic modeling algorithms with UDA, resulting in
improved performances compared to the initial UDA setup.