  Media has a substantial impact on the public perception of events. A
one-sided or polarizing perspective on any topic is usually described as media
bias. One of the ways how bias in news articles can be introduced is by
altering word choice. Biased word choices are not always obvious, nor do they
exhibit high context-dependency. Hence, detecting bias is often difficult. We
propose a Transformer-based deep learning architecture trained via Multi-Task
Learning using six bias-related data sets to tackle the media bias detection
problem. Our best-performing implementation achieves a macro $F_{1}$ of 0.776,
a performance boost of 3\% compared to our baseline, outperforming existing
methods. Our results indicate Multi-Task Learning as a promising alternative to
improve existing baseline models in identifying slanted reporting.
