@inproceedings{lee-etal-2019-team,
    title = "Team yeon-zi at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data",
    author = "Lee, Nayeon  and
      Liu, Zihan  and
      Fung, Pascale",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/S19-2184/",
    doi = "10.18653/v1/S19-2184",
    pages = "1052--1056",
    abstract = "This paper describes our system that has been submitted to SemEval-2019 Task 4: Hyperpartisan News Detection. We focus on removing the noise inherent in the hyperpartisanship dataset from both data-level and model-level by leveraging semi-supervised pseudo-labels and the state-of-the-art BERT model. Our model achieves 75.8{\%} accuracy in the final by-article dataset without ensemble learning."
}