@inproceedings{chen-etal-2020-ntu,
    title = "{NTU}{\_}{NLP} at {S}em{E}val-2020 Task 12: Identifying Offensive Tweets Using Hierarchical Multi-Task Learning Approach",
    author = "Chen, Po-Chun  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    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-1.279/",
    doi = "10.18653/v1/2020.semeval-1.279",
    pages = "2105--2110",
    abstract = "This paper presents our hierarchical multi-task learning (HMTL) and multi-task learning (MTL) approaches for improving the text encoder in Sub-tasks A, B, and C of Multilingual Offensive Language Identification in Social Media (SemEval-2020 Task 12). We show that using the MTL approach can greatly improve the performance of complex problems, i.e. Sub-tasks B and C. Coupled with a hierarchical approach, the performances are further improved. Overall, our best model, HMTL outperforms the baseline model by 3{\%} and 2{\%} of Macro F-score in Sub-tasks B and C of OffensEval 2020, respectively."
}