BRUMS at HASOC 2019: Deep learning models for multilingual hate speech and offensive language identification

Tharindu Ranasinghe; Marcos Zampieri; Hansi Hettiarachchi

BRUMS at HASOC 2019: Deep learning models for multilingual hate speech and offensive language identification

Tharindu Ranasinghe, Marcos Zampieri, Hansi Hettiarachchi

Computer Science

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

Abstract

In this paper, we describe the BRUMS entry to the Hate Speech and Offensive Content Identification in Indo-European Languages
(HASOC) shared task 2019. The HASOC organizers provided participants with annotated datasets containing posts from social media in
English, German, and Hindi (including code-mixing). We present a multilingual deep learning model to identify hate speech and offensive language in social media. Our best performing system was ranked 3rd among 79 entries in the English track of the HASOC sub-task 1.

Original language	English
Title of host publication	Working Notes of FIRE 2019 - Forum for Information Retrieval Evaluation
Publisher	CEUR-WS.org
Pages	199-207
Publication status	Published - Dec 2019

Publication series

Name	CEUR Workshop Proceedings
Publisher	CEUR-WS.org
Volume	2517
ISSN (Electronic)	1613-0073

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https://ceur-ws.org/Vol-2517/T3-3.pdfLicence: CC BY 4.0

Cite this

@inproceedings{184a04fb6c2444d2865283add0fe78fb,

title = "BRUMS at HASOC 2019: Deep learning models for multilingual hate speech and offensive language identification",

abstract = "In this paper, we describe the BRUMS entry to the Hate Speech and Offensive Content Identification in Indo-European Languages(HASOC) shared task 2019. The HASOC organizers provided participants with annotated datasets containing posts from social media inEnglish, German, and Hindi (including code-mixing). We present a multilingual deep learning model to identify hate speech and offensive language in social media. Our best performing system was ranked 3rd among 79 entries in the English track of the HASOC sub-task 1.",

author = "Tharindu Ranasinghe and Marcos Zampieri and Hansi Hettiarachchi",

year = "2019",

month = dec,

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS.org",

pages = "199--207",

booktitle = "Working Notes of FIRE 2019 - Forum for Information Retrieval Evaluation",

}

BRUMS at HASOC 2019: Deep learning models for multilingual hate speech and offensive language identification. / Ranasinghe, Tharindu; Zampieri, Marcos; Hettiarachchi, Hansi.
Working Notes of FIRE 2019 - Forum for Information Retrieval Evaluation. CEUR-WS.org, 2019. p. 199-207 (CEUR Workshop Proceedings; Vol. 2517).

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

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AU - Zampieri, Marcos

AU - Hettiarachchi, Hansi

PY - 2019/12

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N2 - In this paper, we describe the BRUMS entry to the Hate Speech and Offensive Content Identification in Indo-European Languages(HASOC) shared task 2019. The HASOC organizers provided participants with annotated datasets containing posts from social media inEnglish, German, and Hindi (including code-mixing). We present a multilingual deep learning model to identify hate speech and offensive language in social media. Our best performing system was ranked 3rd among 79 entries in the English track of the HASOC sub-task 1.

AB - In this paper, we describe the BRUMS entry to the Hate Speech and Offensive Content Identification in Indo-European Languages(HASOC) shared task 2019. The HASOC organizers provided participants with annotated datasets containing posts from social media inEnglish, German, and Hindi (including code-mixing). We present a multilingual deep learning model to identify hate speech and offensive language in social media. Our best performing system was ranked 3rd among 79 entries in the English track of the HASOC sub-task 1.

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M3 - Conference publication

T3 - CEUR Workshop Proceedings

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BT - Working Notes of FIRE 2019 - Forum for Information Retrieval Evaluation

PB - CEUR-WS.org

ER -

BRUMS at HASOC 2019: Deep learning models for multilingual hate speech and offensive language identification

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