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.

仇恨言论和网络欺凌等网络攻击性内容的广泛传播是一种全球现象。这引发了人们对人工智能 (AI) 和自然语言处理 (NLP) 社区的兴趣，推动了各种经过训练以自动检测潜在有害内容的系统的开发。这些系统需要带注释的数据集来训练机器学习 (ML) 模型。然而，除了一些明显的例外，该主题的大多数数据集都涉及英语和其他一些高资源语言。因此，攻击性语言识别的研究仅限于这些语言。本文通过解决僧伽罗语中的攻击性语言识别问题来解决这一差距，僧伽罗语是一种资源匮乏的印度-雅利安语言，斯里兰卡有超过 1700 万人使用。我们介绍了僧伽罗攻击性语言数据集（SOLD），并在该数据集上进行了多项实验。SOLD是一个手动注释的数据集，包含来自 Twitter 的 10,000 条帖子，在句子级别和标记级别都被注释为攻击性和非攻击性，从而提高了 ML 模型的可解释性。SOLD是第一个为僧伽罗语编译的大型公开可用的攻击性语言数据集。我们还引入了SemiSOLD，这是一个更大的数据集，包含超过 145,000 条僧伽罗语推文，并按照半监督方法进行注释。