@inproceedings{wang-marinho-2020-nova,
    title = "Nova-Wang at {S}em{E}val-2020 Task 12: {O}ffens{E}mblert: An Ensemble of{O}ffensive Language Classifiers",
    author = "Wang, Susan  and
      Marinho, Zita",
    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/fix-sig-urls/2020.semeval-1.207/",
    doi = "10.18653/v1/2020.semeval-1.207",
    pages = "1587--1597",
    abstract = "This paper presents our contribution to the Offensive Language Classification Task (English SubTask A) of Semeval 2020. We propose different Bert models trained on several offensive language classification and profanity datasets, and combine their output predictions in an ensemble model. We experimented with different ensemble approaches, such as SVMs, Gradient boosting, AdaBoosting and Logistic Regression. We further propose an under-sampling approach of the current SOLID dataset, which removed the most uncertain partitions of the dataset, increasing the recall of the dataset. Our best model, an average ensemble of four different Bert models, achieved 11th place out of 82 participants with a macro F1 score of 0.91344 in the English SubTask A."
}