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SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)

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arxiv 2006.07235 v2 pith:3T7BS4XL submitted 2020-06-12 cs.CL

Marcos Zampieri , Preslav Nakov , Sara Rosenthal , Pepa Atanasova , Georgi Karadzhov , Hamdy Mubarak , Leon Derczynski , Zeses Pitenis
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classification cs.CL
keywords taskoffensevalsemeval-2020subtasksacrossenglishfeaturedidentification
verification ladder T0 review T1 audit T2 compute T3 formal
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We present the results and main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2020). The task involves three subtasks corresponding to the hierarchical taxonomy of the OLID schema (Zampieri et al., 2019a) from OffensEval 2019. The task featured five languages: English, Arabic, Danish, Greek, and Turkish for Subtask A. In addition, English also featured Subtasks B and C. OffensEval 2020 was one of the most popular tasks at SemEval-2020 attracting a large number of participants across all subtasks and also across all languages. A total of 528 teams signed up to participate in the task, 145 teams submitted systems during the evaluation period, and 70 submitted system description papers.

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Cited by 2 Pith papers

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  1. Multi-task Learning with Active Learning for Arabic Offensive Speech Detection

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A multi-task Arabic offensive speech detector with entropy-based active learning and weighted emoji tokens reports 85.42% macro F1 on OSACT2022 using roughly 3,300 training samples.

  2. Detoxify: A framework for abusive text transformation using LLMs

    cs.CL 2025-07 reject novelty 3.0 of 10

    A comparative study claims Groq produces the most positive but least semantically faithful detoxified text, but the comparison is undermined by inconsistent methodology.

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