MUDES: Multilingual Detection of Offensive Spans

Ranasinghe, Tharindu and Zampieri, Marcos (2021) MUDES: Multilingual Detection of Offensive Spans. In: NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies, Demonstrations. NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Demonstrations . Association for Computational Linguistics, pp. 144-152. ISBN 9781954085480

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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.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
221498
Deposited By:
Deposited On:
28 Jun 2024 12:50
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Jul 2024 05:29