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A Review of Machine Translation Techniques for Low-Resource Languages
PENG Cheng-xi, MA Zi-han
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DOI:10.17265/2159-5836/2025.09.008
Northwestern Polytechnical University, Xi’an, China
Machine translation of low-resource languages (LRLs) has long been hindered by limited corpora and linguistic complexity. This review summarizes key developments, from traditional methods to recent progress with large language models (LLMs), while highlighting ongoing challenges such as data bottlenecks, biases, fairness, and computational costs. Finally, it discusses future directions, including efficient parameter fine-tuning, multimodal translation, and community-driven corpus construction, providing insights for advancing LRL translation research.
low-resource languages (LRLs) , machine translation, large language models (LLMs)
Journal of Literature and Art Studies, September 2025, Vol. 15, No. 9, 725-731
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