@inproceedings{jiang-etal-2020-umsiforeseer,
    title = "{UMSIF}oreseer at {S}em{E}val-2020 Task 11: Propaganda Detection by Fine-Tuning {BERT} with Resampling and Ensemble Learning",
    author = "Jiang, Yunzhe  and
      Garbacea, Cristina  and
      Mei, Qiaozhu",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.semeval-1.242/",
    doi = "10.18653/v1/2020.semeval-1.242",
    pages = "1841--1846",
    abstract = "We describe our participation at the SemEval 2020 ``Detection of Propaganda Techniques in News Articles'' - Techniques Classification (TC) task, designed to categorize textual fragments into one of the 14 given propaganda techniques. Our solution leverages pre-trained BERT models. We present our model implementations, evaluation results and analysis of these results. We also investigate the potential of combining language models with resampling and ensemble learning methods to deal with data imbalance and improve performance."
}