# A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Media

arXiv: https://arxiv.org/abs/2409.20467 · HTML: https://mlsanity.com/paper/2409.20467/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 51 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [Learning from Rules Generalizing Labeled Exemplars](https://mlsanity.com/paper/2004.06025/) (2020)
- [Self-Training with Weak Supervision](https://mlsanity.com/paper/2104.05514/) (2021)
- [Denoising Multi-Source Weak Supervision for Neural Text Classification](https://mlsanity.com/paper/2010.04582/) (2020)
- [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://mlsanity.com/paper/2109.09701/) (2021)
- [A Large-scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts](https://mlsanity.com/paper/2103.11528/) (2021)
- [Emotion Recognition for Vietnamese Social Media Text](https://mlsanity.com/paper/1911.09339/) (2019)
- [DaN+: Danish Nested Named Entities and Lexical Normalization](https://mlsanity.com/paper/2105.11301/) (2021)
- [Universal Dependency Parsing for Hindi-English Code-switching](https://mlsanity.com/paper/1804.05868/) (2018)
- [ViHOS: Hate Speech Spans Detection for Vietnamese](https://mlsanity.com/paper/2301.10186/) (2023)
- [SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence](https://mlsanity.com/paper/2105.15079/) (2021)
- [ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing](https://mlsanity.com/paper/2310.11166/) (2023)
- [Sequence-to-Sequence Lexical Normalization with Multilingual Transformers](https://mlsanity.com/paper/2110.02869/) (2021)
- [Iterative Data Programming for Expanding Text Classification Corpora](https://mlsanity.com/paper/2002.01412/) (2020)
- [Detecting Spam Reviews on Vietnamese E-commerce Websites](https://mlsanity.com/paper/2207.14636/) (2022)
- [Non-Standard Vietnamese Word Detection and Normalization for Text-to-Speech](https://mlsanity.com/paper/2209.02971/) (2022)
- [Sentiment Analysis of German Twitter](https://mlsanity.com/paper/1911.13062/) (2019)
- [Automatic Textual Normalization for Hate Speech Detection](https://mlsanity.com/paper/2311.06851/) (2023)
- [ViLexNorm: A Lexical Normalization Corpus for Vietnamese Social Media Text](https://mlsanity.com/paper/2401.16403/) (2024)

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

- [ViSoLex: An Open-Source Repository for Vietnamese Social Media Lexical Normalization](https://mlsanity.com/paper/2501.07020/) (2025)

## Abstract (arXiv)

This study introduces an innovative automatic labeling framework to address the challenges of lexical normalization in social media texts for low-resource languages like Vietnamese. Social media data is rich and diverse, but the evolving and varied language used in these contexts makes manual labeling labor-intensive and expensive. To tackle these issues, we propose a framework that integrates semi-supervised learning with weak supervision techniques. This approach enhances the quality of training dataset and expands its size while minimizing manual labeling efforts. Our framework automatically labels raw data, converting non-standard vocabulary into standardized forms, thereby improving the accuracy and consistency of the training data. Experimental results demonstrate the effectiveness of our weak supervision framework in normalizing Vietnamese text, especially when utilizing Pre-trained Language Models. The proposed framework achieves an impressive F1-score of 82.72% and maintains vocabulary integrity with an accuracy of up to 99.22%. Additionally, it effectively handles undiacritized text under various conditions. This framework significantly enhances natural language normalization quality and improves the accuracy of various NLP tasks, leading to an average accuracy increase of 1-3%.