SOLD: Sinhala Offensive Language Dataset

Ranasinghe, Tharindu; Anuradha, Isuri; Damith, Premasiri,; Silva, Kanishka; Hettiarachchi, Hansi; Uyangodage, Lasitha; Zampieri, Marcos

doi:10.48550/arxiv.2212.00851

Cited by 2 publications

(1 citation statement)

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“…Introduced in (Zampieri et al 2019a), the OLID taxonomy is a labeling schema that classifies each example for offensiveness using the following three-level hierarchy. The original OLID dataset was created for English and the taxonomy has been widely adopted for several languages (Pitenis, Zampieri, and Ranasinghe 2020;Gaikwad et al 2021;Ranasinghe et al 2022a). The popularity of OLID is due to the flexibility provided by its hierarchical annotation model that considers multiple types of offensive content in a single taxonomy.…”

Section: The Olid Taxonomymentioning

confidence: 99%

OffensEval 2023: Offensive language identification in the age of Large Language Models

Zampieri,

Rosenthal,

Nakov

et al. 2023

Nat. Lang. Eng.

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The OffensEval shared tasks organized as part of SemEval-2019–2020 were very popular, attracting over 1300 participating teams. The two editions of the shared task helped advance the state of the art in offensive language identification by providing the community with benchmark datasets in Arabic, Danish, English, Greek, and Turkish. The datasets were annotated using the OLID hierarchical taxonomy, which since then has become the de facto standard in general offensive language identification research and was widely used beyond OffensEval. We present a survey of OffensEval and related competitions, and we discuss the main lessons learned. We further evaluate the performance of Large Language Models (LLMs), which have recently revolutionalized the field of Natural Language Processing. We use zero-shot prompting with six popular LLMs and zero-shot learning with two task-specific fine-tuned BERT models, and we compare the results against those of the top-performing teams at the OffensEval competitions. Our results show that while some LMMs such as Flan-T5 achieve competitive performance, in general LLMs lag behind the best OffensEval systems.

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Section: The Olid Taxonomymentioning

confidence: 99%

OffensEval 2023: Offensive language identification in the age of Large Language Models

Zampieri,

Rosenthal,

Nakov

et al. 2023

Nat. Lang. Eng.

Self Cite

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A survey on multi-lingual offensive language detection

Mnassri,

Farahbakhsh,

Chalehchaleh

et al. 2024

PeerJ Computer Science

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The prevalence of offensive content on online communication and social media platforms is growing more and more common, which makes its detection difficult, especially in multilingual settings. The term “Offensive Language” encompasses a wide range of expressions, including various forms of hate speech and aggressive content. Therefore, exploring multilingual offensive content, that goes beyond a single language, focus and represents more linguistic diversities and cultural factors. By exploring multilingual offensive content, we can broaden our understanding and effectively combat the widespread global impact of offensive language. This survey examines the existing state of multilingual offensive language detection, including a comprehensive analysis on previous multilingual approaches, and existing datasets, as well as provides resources in the field. We also explore the related community challenges on this task, which include technical, cultural, and linguistic ones, as well as their limitations. Furthermore, in this survey we propose several potential future directions toward more efficient solutions for multilingual offensive language detection, enabling safer digital communication environment worldwide.

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SOLD: Sinhala Offensive Language Dataset

Cited by 2 publications

References 0 publications

OffensEval 2023: Offensive language identification in the age of Large Language Models

OffensEval 2023: Offensive language identification in the age of Large Language Models

A survey on multi-lingual offensive language detection

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