Jul 2026· Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi· Vol 5, pp. 138-145· 0 citations· 27 references
TL;DR
The findings show that a hybrid combination of TL and DA with a self-supervised objective is the most effective solution for extremely low-resource scenarios, capable of producing the highest translation quality and outperforming baseline models and traditional methods such as Statistical Machine Translation (SMT).
Abstract
Modern Neural Machine Translation (NMT) systems have achieved state-of-the-art, performance, largely due to the availability of large-scale parallel corpora. However, the translation quality of NMT for Low-Resource Languages (LRL) remains limited due to data sparsity. Numerous studies have proposed different strategies to address this challenge. Among the most widely adopted strategies are Transfer Learning (TL) and Data Augmentation (DA) strategies. This research aims to present a systematic review of how these techniques, including Back-Translation (BT), Hybrid Transfer Learning (HTL), and the utilization of self-supervised objectives such as Masked Language Modeling (MLM), Causal Language Modeling (CLM), and Denoising Autoencoder (DAE), affect the quality improvement of NMT for LRL. The findings show that a hybrid combination of TL and DA with a self-supervised objective is the most effective solution for extremely low-resource scenarios, capable of producing the highest translation quality (highest BLEU score) and outperforming baseline models and traditional methods such as Statistical Machine Translation (SMT).
The rapid evolution of AI-driven language technologies has inadvertently widened the gap between high-resource and marginalised languages. Despite significant progress in AI-driven translation for high-resource languages, low-resource languages remain underrepresented due to limited data, a lack of benchmarks, and evaluation challenges. This study presents a comprehensive systematic review of machine translation for low-resource languages, focusing on advances in neural machine translation (NMT) and large language models (LLMs) between 2017 and 2025. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 63 studies were selected from the 1696 articles in the Scopus, Web of Science, and Google Scholar databases. The review identifies five dominant methodological approaches: data augmentation, back-translation, transfer learning, pre-training, and parameter-efficient fine-tuning. The findings reveal that model performance is highly dependent on resource availability: transformer-based NMT excels in moderate data settings, while LLMs demonstrate promising zero-shot and few-shot capabilities in extremely low-resource scenarios. Hybrid NMT–LLM approaches emerge as a particularly effective paradigm. The study also highlights critical challenges, including the absence of standardised benchmarks, over-reliance on inadequate evaluation metrics such as Bilingual Evaluation Understudy (BLEU), limited human evaluation, and significant geographic and linguistic underrepresentation. Additionally, ethical concerns related to bias, cultural representation, and community engagement are increasingly relevant. The findings contribute to advancing inclusive and equitable AI-driven language technologies.
Sweet Agrawal, A. Agbeyangi· Technologies· 0 citations
Neural machine translation (NMT) has witnessed substantial advancements, leveraging its learning capability to deliver highly accurate translations. Nevertheless, the efficacy of NMT models is contingent upon the accessibility of extensive-scale, high-quality training data, and its performance suffers notably in the absence of such datasets. To tackle this challenge, we propose a semantic distance augmentation (SDA) method that integrates syntactic information from constituency parse trees into the NMT encoder to optimize self-attention. Specifically, the source language sentences in the training set are analyzed by constituency parse analysis and the semantic distance attention matrix is constructed. Then, a fusion strategy is designed to integrate this matrix into the self-attention weight, enhancing the representation of the source sentences. In addition, a SDA length-aware strategy is proposed to adaptively control the contribution of semantic distance in the attention computation. Empirical evaluations across multiple low-resource language pairs reveal that the SDA method achieves statistically significant improvements in translation quality over the strong baseline, without requiring additional training data or increasing model complexity.
Fuxue Li, Hong Yan, Chuncheng Chi et al.· PeerJ Computer Science· 0 citations
Neural machine translation (NMT) systems are widely used, but their performance remains strongly dependent on the availability of large-scale digital corpora, making translation for low-resource languages a persistent challenge. In parallel, large language models (LLMs) have recently emerged as a promising paradigm for multilingual text generation and translation; however, their behavior in low-resource settings remains largely underexplored. The challenge becomes even more acute for historical languages. Chagatai, a historical Turkic literary language of Central Asia with no native speakers, unstable orthography, and parallel data, represents an extreme case of such a condition. This study investigates whether transliteration significantly affects translation performance and how LLM-based and NMT-based systems compare under an extremely low-resource setting. To address these questions, we evaluated four source-text configurations (original Arabic script, expert manual transliteration, LLM-based transliteration, and rule-based Uroman transliteration) for translation into six target languages: Kazakh, English, Uzbek, Uyghur, Turkish, Russian, and Arabic. The results show that manual transliteration consistently yields the best translation performance, while noisy automatic romanization reduces these gains. For model comparison, GPT-4o was assessed alongside two fine-tuned NMT baselines, NLLB and TranslateGemma. The findings further show that LLM-based translation can be competitive with, and in some settings outperform, fine-tuned NMT systems, although this advantage comes with lower interpretability. Overall, these findings show that, for extremely low-resource historical languages written in non-Latin scripts, source-side representation is a decisive factor and may be as important as the choice of translation model itself.
A. Mansurova, Meruert Bekmukhamedova, Bekarys Baibolat et al.· Electronics· 0 citations
The effectiveness of multilingual transfer learning in low-resource settings is demonstrated by the fine-tuned Multilingual Bidirectional and Auto-Regressive Transformer-50 model, significantly outperforming the pretrained baseline.
G. Harshitha, Vasudeva, Nisha P. Poojary et al.· Engineering, Technology &...· 0 citations
This systematic review examines recent progress in the pretraining and adaptation of LLMs for Low-Resource Languages (LRLs) and focuses on the ethics in AI practice, the development of corpora through communities, and interdisciplinary research collaboration among computational linguists, social scientists, and digital humanists.
Ismail Hossain, Mridul Banik, Fahmid Al Farid et al.· Computer Modeling in Enginee...· 0 citations
This paper introduces *TranslatePsy-AfriSLM*, a collection of open-source MT resources for 19 Sub-Saharan African languages, including curated parallel data, African-specialized synthetic data, and a family of fine-tuned SLMs.
Milan Gritta, Patrik Lambert, Jihye Back et al.· 0 citations