SOLD: Sinhala offensive language dataset

Abstract

The widespread of offensive content online, such as hate speech and cyber-bullying, is a global phenomenon. This has sparked interest in the artificial intelligence (AI) and natural language processing (NLP) communities, motivating the development of various systems trained to detect potentially harmful content automatically. These systems require annotated datasets to train the machine learning (ML) models. However, with a few notable exceptions, most datasets on this topic have dealt with English and a few other high-resource languages. As a result, the research in offensive language identification has been limited to these languages. This paper addresses this gap by tackling offensive language identification in Sinhala, a low-resource Indo-Aryan language spoken by over 17 million people in Sri Lanka. We introduce the Sinhala Offensive Language Dataset (SOLD) and present multiple experiments on this dataset. SOLD is a manually annotated dataset containing 10,000 posts from Twitter annotated as offensive and not offensive at both sentence-level and token-level, improving the explainability of the ML models. SOLD is the first large publicly available offensive language dataset compiled for Sinhala. We also introduce SemiSOLD, a larger dataset containing more than 145,000 Sinhala tweets, annotated following a semi-supervised approach.

Publication DOI: https://doi.org/10.1007/s10579-024-09723-1
Divisions: College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Applied AI & Robotics
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies
Additional Information: Copyright © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/
Uncontrolled Keywords: Offensive language identification,Low-resource languages,Deep learning,Transformers
Publication ISSN: 1572-8412
Data Access Statement: Data is available at https://huggingface.co/sinhala-nlp. Code is available at https://github.com/Sinhala-NLP/SOLD.
Last Modified: 14 May 2024 07:31
Date Deposited: 12 Mar 2024 18:16
Full Text Link:
Related URLs: https://link.sp ... 579-024-09723-1 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-03-06
Published Online Date: 2024-03-06
Accepted Date: 2024-01-12
Authors: Ranasinghe, Tharindu (ORCID Profile 0000-0003-3207-3821)
Anuradha, Isuri
Premasiri, Damith
Silva, Kanishka
Hettiarachchi, Hansi
Uyangodage, Lasitha
Zampieri, Marcos

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