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POLITICS, a pretrained model on English news articles of politics, is produced via continued training on RoBERTa, based on a Pretraining Objective Leveraging Inter-article Triplet-loss using Ideological Content and Story.

Details of our proposed training objectives (i.e., Ideology-driven Pretraining Objectives) and experimental results of POLITICS can be found in our NAACL-2022 Findings paper and GitHub Repo.

Together with POLITICS, we also release our curated large-scale dataset (i.e., BIGNEWS) for pretraining, consisting of more than 3.6M political news articles. This asset can be requested here.


Please cite our paper if you use the POLITICS model:

    title = "POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection",
    author = "Liu, Yujian and
    Zhang, Xinliang Frederick and
    Wegsman, David and
    Beauchamp, Nicholas and 
    Wang, Lu"
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
    year = "2022",
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