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.

翻译：社交媒体数据是研究的重要资源，但其中包含大量非标准词汇（NSW）。这些不规范现象阻碍了自然语言处理工具的有效运行。当前针对越南语的最先进方法将这一问题视为词汇规范化任务，涉及手工规则制定或实施多阶段深度学习框架，需要投入大量精力来构建复杂规则。相比之下，我们的方法更为直接，仅采用序列到序列（Seq2Seq）模型。在本研究中，我们提供了一个文本规范化数据集，包含2,181条人工标注的评论，标注者间一致性达0.9014。通过利用Seq2Seq模型进行文本规范化，我们的结果显示准确率略低于70%。尽管如此，文本规范化将仇恨言论检测（HSD）任务的准确率提升了约2%，展示了其在改善复杂NLP任务性能方面的潜力。我们的数据集可供研究使用。