MUDES: Multilingual Detection of Offensive Spans

Tharindu Ranasinghe; Marcos Zampieri

doi:10.18653/v1/2021.naacl-demos.17

MUDES: Multilingual Detection of Offensive Spans

Research output: Chapter in Book/Published conference output › Conference publication

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.

Original language	English
Title of host publication	NAACL: North American Chapter of the Association for Computational Linguistics
Publisher	Association for Computational Linguistics (ACL)
Pages	144-152
Number of pages	9
DOIs	https://doi.org/10.18653/v1/2021.naacl-demos.17
Publication status	Published - Jun 2021
Event	2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations - online Duration: 6 Jun 2021 → 11 Jun 2021 https://2021.naacl.org/#:~:text=NAACL%2DHLT%202021%20is%20currently,6th%20to%20June%2011th%2C%202021.

Conference

Conference	2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations
Period	6/06/21 → 11/06/21
Internet address	https://2021.naacl.org/#:~:text=NAACL%2DHLT%202021%20is%20currently,6th%20to%20June%2011th%2C%202021.

Bibliographical note

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10.18653/v1/2021.naacl-demos.17Licence: CC BY 4.0

2021.naacl-demos.17
ACL materials are Copyright © 1963–2023 ACL; Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
Final published version, 718 KBLicence: CC BY 4.0

Cite this

@inproceedings{256eb92e940b47d5818fb81712124688,

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{\textquoteright} components is presented in this paper.",

author = "Tharindu Ranasinghe and Marcos Zampieri",

note = "ACL materials are Copyright {\textcopyright} 1963–2023 ACL; Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.; 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations ; Conference date: 06-06-2021 Through 11-06-2021",

year = "2021",

month = jun,

doi = "10.18653/v1/2021.naacl-demos.17",

language = "English",

pages = "144--152",

booktitle = "NAACL: North American Chapter of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics (ACL)",

address = "United States",

url = "https://2021.naacl.org/#:~:text=NAACL%2DHLT%202021%20is%20currently,6th%20to%20June%2011th%2C%202021.",

}

Ranasinghe, T & Zampieri, M 2021, MUDES: Multilingual Detection of Offensive Spans. in NAACL: North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics (ACL), pp. 144-152, 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations, 6/06/21. https://doi.org/10.18653/v1/2021.naacl-demos.17

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AU - Zampieri, Marcos

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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.

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DO - 10.18653/v1/2021.naacl-demos.17

M3 - Conference publication

SP - 144

EP - 152

BT - NAACL: North American Chapter of the Association for Computational Linguistics

PB - Association for Computational Linguistics (ACL)

T2 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

Y2 - 6 June 2021 through 11 June 2021

ER -

MUDES: Multilingual Detection of Offensive Spans

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