@inproceedings{zhang-etal-2019-ubc,
    title = "{UBC}-{NLP} at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection With Attention-Based {B}i-{LSTM}s",
    author = "Zhang, Chiyu  and
      Rajendran, Arun  and
      Abdul-Mageed, Muhammad",
    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/fix-sig-urls/S19-2188/",
    doi = "10.18653/v1/S19-2188",
    pages = "1072--1077",
    abstract = "We present our deep learning models submitted to the SemEval-2019 Task 4 competition focused at Hyperpartisan News Detection. We acquire best results with a Bi-LSTM network equipped with a self-attention mechanism. Among 33 participating teams, our submitted system ranks top 7 (65.3{\%} accuracy) on the `labels-by-publisher' sub-task and top 24 out of 44 teams (68.3{\%} accuracy) on the `labels-by-article' sub-task (65.3{\%} accuracy). We also report a model that scores higher than the 8th ranking system (78.5{\%} accuracy) on the `labels-by-article' sub-task."
}