  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.
