Standard
MUDES: Multilingual Detection of Offensive Spans. /
Ranasinghe, Tharindu; Zampieri, Marcos.
NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Demonstrations. Association for Computational Linguistics, 2021. p. 144-152 (NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Demonstrations).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Ranasinghe, T & Zampieri, M 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, 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics,
6/06/21.
https://doi.org/10.18653/v1/2021.naacl-demos.17
APA
Vancouver
Ranasinghe T, Zampieri M.
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. Association for Computational Linguistics. 2021. p. 144-152. (NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Demonstrations). doi: 10.18653/v1/2021.naacl-demos.17
Author
Ranasinghe, Tharindu ; Zampieri, Marcos. /
MUDES: Multilingual Detection of Offensive Spans. NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Demonstrations. Association for Computational Linguistics, 2021. pp. 144-152 (NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Demonstrations).
Bibtex
@inproceedings{d0e84594051c46a2a5795b91f98178ba,
title = "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.",
author = "Tharindu Ranasinghe and Marcos Zampieri",
year = "2021",
month = jun,
day = "1",
doi = "10.18653/v1/2021.naacl-demos.17",
language = "English",
series = "NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Demonstrations",
publisher = "Association for Computational Linguistics",
pages = "144--152",
booktitle = "NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics",
note = "2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL2021 ; Conference date: 06-06-2021 Through 11-06-2021",
url = "https://2021.naacl.org",
}
RIS
TY - GEN
T1 - MUDES: Multilingual Detection of Offensive Spans
AU - Ranasinghe, Tharindu
AU - Zampieri, Marcos
PY - 2021/6/1
Y1 - 2021/6/1
N2 - 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.
AB - 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.
U2 - 10.18653/v1/2021.naacl-demos.17
DO - 10.18653/v1/2021.naacl-demos.17
M3 - Conference contribution/Paper
T3 - NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Demonstrations
SP - 144
EP - 152
BT - NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics
T2 - 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Y2 - 6 June 2021 through 11 June 2021
ER -
