phusroyal/ViHOS

Tasks

• text-classification
• token-classification

Citation text from the card

@inproceedings{hoang-etal-2023-vihos,
    title = "{V}i{HOS}: Hate Speech Spans Detection for {V}ietnamese",
    author = "Hoang, Phu Gia  and
      Luu, Canh Duc  and
      Tran, Khanh Quoc  and
      Nguyen, Kiet Van  and
      Nguyen, Ngan Luu-Thuy",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-main.47",
    doi = "10.18653/v1/2023.eacl-main.47",
    pages = "652--669",
    abstract = "The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems. To help address this issue, we present the ViHOS (Vietnamese Hate and Offensive Spans) dataset, the first human-annotated corpus containing 26k spans on 11k comments. We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Besides, we conduct experiments with various state-of-the-art models. Specifically, XLM-R{\_}Large achieved the best F1-scores in Single span detection and All spans detection, while PhoBERT{\_}Large obtained the highest in Multiple spans detection. Finally, our error analysis demonstrat

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