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Integrating Hybrid AI Approaches for Enhanced Translation in Minority Languages

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Publicado en:Applied Sciences vol. 15, no. 16 (2025), p. 9039-9055
Autor principal: Chen-Chi, Chang
Otros Autores: Yu-Hsun, Lin, Yun-Hsiang, Hsu, I-Hsin, Fan
Publicado:
MDPI AG
Materias:
Language
Language revitalization
Hakka Chinese
Grammatical aspect
Artificial intelligence
Recursion
Deep learning
Minority languages
Communication
Minority & ethnic groups
Parallel corpora
Corpus linguistics
Machine translation
Neural networks
Linguistics
Natural language processing
Literature reviews
Multiculturalism & pluralism
Segmentation
Dialects
Translation methods and strategies
Cultural heritage
Training
Scarcity
Cultural maintenance
Access
Learning
Data
Frame analysis
Languages
Preservation
Translation
Public policy
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Resumen:The proposed hybrid AI-driven translation system’s architecture integrates phrase-based machine translation (PBMT) and neural machine translation (NMT) within a recursive learning framework. It provides a blueprint for institutions that digitize, translate, or teach under-resourced languages. Due to its ability to adapt to multilingual inputs and preserve cultural expressions, it is highly suitable for applications in education, community media, cultural preservation, and government-supported language revitalization initiatives. This study presents a hybrid artificial intelligence model designed to enhance translation quality for low-resource languages, specifically targeting the Hakka language. The proposed model integrates phrase-based machine translation (PBMT) and neural machine translation (NMT) within a recursive learning framework. The methodology consists of three key stages: (1) initial translation using PBMT, where Hakka corpus data is structured into a parallel dataset; (2) NMT training with Transformers, leveraging the generated parallel corpus to train deep learning models; and (3) recursive translation refinement, where iterative translations further enhance model accuracy by expanding the training dataset. This study employs preprocessing techniques to clean and optimize the dataset, reducing noise and improving sentence segmentation. A BLEU score evaluation is conducted to compare the effectiveness of PBMT and NMT across various corpus sizes, demonstrating that while PBMT performs well with limited data, the Transformer-based NMT achieves superior results as training data increases. The findings highlight the advantages of a hybrid approach in overcoming data scarcity challenges for minority languages. This research contributes to machine translation methodologies by proposing a scalable framework for improving linguistic accessibility in under-resourced languages.
ISSN:2076-3417
DOI:10.3390/app15169039
Fuente:Publicly Available Content Database