Abusive Span Detection for Vietnamese Narrative Texts
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Abstract (arXiv)
Abuse in its various forms, including physical, psychological, verbal, sexual, financial, and cultural, has a negative impact on mental health. However, there are limited studies on applying natural language processing (NLP) in this field in Vietnam. Therefore, we aim to contribute by building a human-annotated Vietnamese dataset for detecting abusive content in Vietnamese narrative texts. We sourced these texts from VnExpress, Vietnam's popular online newspaper, where readers often share stories containing abusive content. Identifying and categorizing abusive spans in these texts posed significant challenges during dataset creation, but it also motivated our research. We experimented with lightweight baseline models by freezing PhoBERT and XLM-RoBERTa and using their hidden states in a BiLSTM to assess the complexity of the dataset. According to our experimental results, PhoBERT outperforms other models in both labeled and unlabeled abusive span detection tasks. These results indicate that it has the potential for future improvements.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 42 references that are in the map. First posted on arXiv 13 Dec 2023.
Builds on
15 of 42 references.
References · 15
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- 2019SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)cited by 207
- 2020PhoBERT: Pre-trained language models for Vietnamesecited by 149
- 2020SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)cited by 138
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- 2021Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamesecited by 17
3 more
- 2023ViHOS: Hate Speech Spans Detection for Vietnamesecited by 13
- 2020Best Practices for Managing Data Annotation Projectscited by 12
- 2020HSD Shared Task in VLSP Campaign 2019:Hate Speech Detection for Social Goodcited by 11
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