@inproceedings{dong-choi-2020-xd,
    title = "{XD} at {S}em{E}val-2020 Task 12: Ensemble Approach to Offensive Language Identification in Social Media Using Transformer Encoders",
    author = "Dong, Xiangjue  and
      Choi, Jinho D.",
    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-1.299/",
    doi = "10.18653/v1/2020.semeval-1.299",
    pages = "2244--2250",
    abstract = "This paper presents six document classification models using the latest transformer encoders and a high-performing ensemble model for a task of offensive language identification in social media. For the individual models, deep transformer layers are applied to perform multi-head attentions. For the ensemble model, the utterance representations taken from those individual models are concatenated and fed into a linear decoder to make the final decisions. Our ensemble model outperforms the individual models and shows up to 8.6{\%} improvement over the individual models on the development set. On the test set, it achieves macro-F1 of 90.9{\%} and becomes one of the high performing systems among 85 participants in the sub-task A of this shared task. Our analysis shows that although the ensemble model significantly improves the accuracy on the development set, the improvement is not as evident on the test set."
}