@inproceedings{hou-chen-2019-caunlp,
    title = "{CAU}n{LP} at {NLP}4{IF} 2019 Shared Task: Context-Dependent {BERT} for Sentence-Level Propaganda Detection",
    author = "Hou, Wenjun  and
      Chen, Ying",
    editor = "Feldman, Anna  and
      Da San Martino, Giovanni  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Brew, Chris  and
      Leberknight, Chris  and
      Nakov, Preslav",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/D19-5010/",
    doi = "10.18653/v1/D19-5010",
    pages = "83--86",
    abstract = "The goal of fine-grained propaganda detection is to determine whether a given sentence uses propaganda techniques (sentence-level) or to recognize which techniques are used (fragment-level). This paper presents the sys- tem of our participation in the sentence-level subtask of the propaganda detection shared task. In order to better utilize the document information, we construct context-dependent input pairs (sentence-title pair and sentence- context pair) to fine-tune the pretrained BERT, and we also use the undersampling method to tackle the problem of imbalanced data."
}