readme: ---
  language:
  - en
  tags:
  - politics
  - roberta
  license:
  - cc-by-nc-sa-4.0
  pipeline_tag: text-classification
  ---
  
  ## POLITICS
  POLITICS, a pretrained model on English news articles of politics, is produced via continued training on RoBERTa, based on a **P**retraining **O**bjective **L**everaging **I**nter-article **T**riplet-loss using **I**deological **C**ontent and **S**tory. 
  
  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](https://aclanthology.org/2022.findings-naacl.101.pdf) and GitHub [Repo](https://github.com/launchnlp/POLITICS).
  
  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](https://docs.google.com/forms/d/e/1FAIpQLSf4hft2AHbuak8jHcltVec_2HviaBBVKXPN4OC-CuW4OFORsw/viewform).
  
  ## Citation
  Please cite our paper if you use the **POLITICS** model:
  ```
  @inproceedings{liu-etal-2022-POLITICS,
      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",
  ```