Using transformer-based models for Vietnamese language detection.

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.
• 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.