MUDES: Multilingual Detection of Offensive Spans

Tharindu Ranasinghe; Marcos Zampieri

NAACL 2021system demonstrations44 citations

MUDES: Multilingual Detection of Offensive Spans

Abstract

The interest in offensive content identification in social media has grown substantially in recent years. Previous work has dealt mostly with post level annotations. However, identifying offensive spans is useful in many ways. To help coping with this important challenge, we present MUDES, a multilingual system to detect offensive spans in texts. MUDES features pre-trained models, a Python API for developers, and a user-friendly web-based interface. A detailed description of MUDES’ components is presented in this paper.

BibTeX

@inproceedings{ranasinghe-zampieri-2021-mudes,
    title = "{MUDES}: Multilingual Detection of Offensive Spans",
    author = "Ranasinghe, Tharindu  and
      Zampieri, Marcos",
    editor = "Sil, Avi  and
      Lin, Xi Victoria",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-demos.17/",
    doi = "10.18653/v1/2021.naacl-demos.17",
    pages = "144--152"
}