  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.
