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Using transformer-based models for Vietnamese language detection.

Son Tran1, Phuoc Tran1

  • 1Natural Language Processing and Knowledge Discovery Research Group, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

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|February 13, 2026
PubMed
Summary
This summary is machine-generated.

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This study presents a new method for detecting Vietnamese text, addressing challenges posed by its unique orthography and diacritics. The approach enhances Transformer models for accurate language identification.

Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Language Identification

Background:

  • Vietnamese presents unique orthographic challenges due to its use of Latin characters with diacritics.
  • Accurate semantic distinction in Vietnamese heavily relies on accent marks, complicating text analysis.
  • Existing NLP models may struggle with these specific linguistic features.

Purpose of the Study:

  • To introduce a novel solution for detecting Vietnamese text based on orthographic and contextual features.
  • To investigate the impact of Vietnamese linguistic characteristics on Transformer-based NLP models.
  • To enhance the capability of NLP models in distinguishing Vietnamese text.

Main Methods:

  • Examined specific challenges of Vietnamese orthography and word formation.

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  • Proposed a new approach to improve Vietnamese text detection.
  • Utilized Transformer-based models, known for superior performance in NLP tasks.
  • Evaluated the approach on a benchmark dataset.

    • Main Results:

    • The proposed approach demonstrated high accuracy and robustness in Vietnamese text detection.
    • The method outperformed conventional techniques for language identification.
    • Transformer-based models were shown to effectively learn Vietnamese orthographic and contextual patterns.

    Conclusions:

    • The developed method significantly improves Vietnamese text detection.
    • Transformer models are effective for analyzing complex languages like Vietnamese.
    • This work contributes to advancements in multilingual NLP processing and language identification.