MUDES: Multilingual Detection of Offensive Spans

Tharindu Ranasinghe, Marcos Zampieri


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.
Anthology ID:
2021.naacl-demos.17
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
144–152
Language:
URL:
https://aclanthology.org/2021.naacl-demos.17
DOI:
10.18653/v1/2021.naacl-demos.17
Bibkey:
Cite (ACL):
Tharindu Ranasinghe and Marcos Zampieri. 2021. MUDES: Multilingual Detection of Offensive Spans. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations, pages 144–152, Online. Association for Computational Linguistics.
Cite (Informal):
MUDES: Multilingual Detection of Offensive Spans (Ranasinghe & Zampieri, NAACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.naacl-demos.17.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2021.naacl-demos.17.mp4
Code
 tharindudr/MUDES
Data
OLID