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arXiv · 2311.06851

Automatic Textual Normalization for Hate Speech Detection

Anh Thi-Hoang Nguyen·Dung Ha Nguyen·Nguyet Thi Nguyen·Khanh Thanh-Duy Ho·Kiet Van Nguyen
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Abstract

Social media data is a valuable resource for research, yet it contains a wide range of non-standard words (NSW). These irregularities hinder the effective operation of NLP tools. Current state-of-the-art methods for the Vietnamese language address this issue as a problem of lexical normalization, involving the creation of manual rules or the implementation of multi-staged deep learning frameworks, which necessitate extensive efforts to craft intricate rules. In contrast, our approach is straightforward, employing solely a sequence-to-sequence (Seq2Seq) model. In this research, we provide a dataset for textual normalization, comprising 2,181 human-annotated comments with an inter-annotator agreement of 0.9014. By leveraging the Seq2Seq model for textual normalization, our results reveal that the accuracy achieved falls slightly short of 70%. Nevertheless, textual normalization enhances the accuracy of the Hate Speech Detection (HSD) task by approximately 2%, demonstrating its potential to improve the performance of complex NLP tasks. Our dataset is accessible for research purposes.

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Anh Thi-Hoang Nguyen, Dung Ha Nguyen, Nguyet Thi Nguyen, Khanh Thanh-Duy Ho, Kiet Van Nguyen. 2023-11-12. Automatic Textual Normalization for Hate Speech Detection. https://doi.org/10.1007/978-3-031-64779-6_1

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