@inproceedings{daval-frerot-weis-2020-wmd,
    title = "{WMD} at {S}em{E}val-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation",
    author = "Daval-Frerot, Guillaume  and
      Weis, Yannick",
    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/jlcl-multiple-ingestion/2020.semeval-1.246/",
    doi = "10.18653/v1/2020.semeval-1.246",
    pages = "1865--1874",
    abstract = "In this work, we combine the state-of-the-art BERT architecture with the semi-supervised learning technique UDA in order to exploit unlabeled raw data to assess humor and detect propaganda in the tasks 7 and 11 of the SemEval-2020 competition. The use of UDA shows promising results with a systematic improvement of the performances over the four different subtasks, and even outperforms supervised learning with the additional labels of the Funlines dataset."
}