  We address an important gap in detecting political bias in news articles.
Previous works that perform document classification can be influenced by the
writing style of each news outlet, leading to overfitting and limited
generalizability. Our approach overcomes this limitation by considering both
the sentence-level semantics and the document-level rhetorical structure,
resulting in a more robust and style-agnostic approach to detecting political
bias in news articles. We introduce a novel multi-head hierarchical attention
model that effectively encodes the structure of long documents through a
diverse ensemble of attention heads. While journalism follows a formalized
rhetorical structure, the writing style may vary by news outlet. We demonstrate
that our method overcomes this domain dependency and outperforms previous
approaches for robustness and accuracy. Further analysis and human evaluation
demonstrate the ability of our model to capture common discourse structures in
journalism. Our code is available at:
https://github.com/xfactlab/emnlp2023-Document-Hierarchy
