# Automatic Textual Normalization for Hate Speech Detection

arXiv: https://arxiv.org/abs/2311.06851 · HTML: https://mlsanity.com/paper/2311.06851/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 12 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [A Large-scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts](https://mlsanity.com/paper/2103.11528/) (2021)
- [Adapting Sequence to Sequence models for Text Normalization in Social Media](https://mlsanity.com/paper/1904.06100/) (2019)
- [Exploiting Vietnamese Social Media Characteristics for Textual Emotion Recognition in Vietnamese](https://mlsanity.com/paper/2009.11005/) (2020)
- [VSEC: Transformer-based Model for Vietnamese Spelling Correction](https://mlsanity.com/paper/2111.00640/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Media](https://mlsanity.com/paper/2409.20467/) (2024)

## Abstract (arXiv)

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.