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Natural Language Processing in Low-Resource Languages: Progress and Prospects

Author(s):Ritul Phukan1, Monalisa Daimari2, Anupam Kharghoria3, Biman Basumatary3

Affiliation: 1,2,3,4Department of Computer Science and Engineering, Assam Down Town University, Guwahati, India

Page No: 4-8

Volume issue & Publishing Year: Volume 2 Issue 9 ,Sep -2025

Journal: International Journal of Advanced Multidisciplinary Application.(IJAMA)

ISSN NO: 3048-9350

DOI: https://doi.org/10.5281/zenodo.17582873

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Abstract:
Low-resource languages languages with limited annotated corpora, lexicons, and digital resources pose major challenges for modern natural language processing (NLP). Recent progress in transfer learning, multilingual pretraining, parameter-efficient adaptation, data augmentation, and community-driven dataset creation has substantially improved capabilities for many such languages, yet large performance gaps remain compared to high-resource languages. This article surveys the technical advances that enable NLP for low-resource languages (including unsupervised and weakly supervised methods, multilingual and massively multilingual models, few-shot and in-context learning with large language models, and adapter/LoRA-style parameter-efficient fine-tuning). We examine practical pipelines for tasks such as machine translation, speech recognition, OCR, and information extraction; describe prominent dataset and community projects; summarize typical evaluation strategies and their pitfalls; and outline promising research directions (community data collection, privacy-preserving methods, on-device adaptation, and ethics-aware deployments). The review highlights approaches that balance performance, compute cost, and data-efficiency, and recommends research and deployment practices to accelerate inclusive language technology.

Keywords: Low-resource languages, transfer learning, multilingual pretraining, few-shot learning, LoRA / adapters, data augmentation, machine translation, speech datasets, Masakhane, Common Voice

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