Abusive Span Detection for Vietnamese Narrative Texts

Abstract

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.

arXiv commentAccepted at SoICT 2023

References in corpus (6)

• All You Need is "Love": Evading Hate-speech Detection
• A Large-scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts
• HSD Shared Task in VLSP Campaign 2019:Hate Speech Detection for Social Good
• SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
• SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection
• Best Practices for Managing Data Annotation Projects