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--- |
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language: |
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- en |
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tags: |
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- politics |
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- roberta |
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license: |
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- cc-by-nc-sa-4.0 |
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--- |
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## POLITICS |
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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. |
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**ALERT:** POLITICS is a pre-trained **language model** that specializes in comprehending news articles and understanding ideological content. However, POLITICS cannot be used **out-of-the-box** on downstream tasks such as predicting ideological leanings and discerning stances expressed in texts. To perform predictions on downstream tasks, you are advised to **fine-tune** POLITICS on your own dataset first. |
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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). |
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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). |
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## Citation |
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Please cite our paper if you use the **POLITICS** model: |
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``` |
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@inproceedings{liu-etal-2022-POLITICS, |
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title = "POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection", |
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author = "Liu, Yujian and |
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Zhang, Xinliang Frederick and |
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Wegsman, David and |
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Beauchamp, Nicholas and |
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Wang, Lu" |
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booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022", |
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year = "2022", |
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``` |
