Tafa, Taofik O. and Hashim, Siti Zaiton Mohd and Othman, Mohd Shahizan and Alhussian, Hitham and Nasser, Maged and Abdulkadir, Said Jadid and Huspi, Sharin Hazlin and Adeyemo, Sarafa O. and Bena, Yunusa Adamu (2025) Machine Translation Performance for Low-Resource Languages: A Systematic Literature Review. IEEE Access, 13. 72486 – 72505. ISSN 21693536
Full text not available from this repository.Abstract
Machine translation (MT) for low-resource languages continues to face significant challenges because of limited digital resources and parallel corpora, despite remarkable developments in neural machine translation (NMT). Addressing these challenges requires a thorough review of existing research to identify effective strategies and methods. To achieve this, a systematic literature review (SLR) is conducted following PRISMA guidelines and systematically analysing studies published in various academic databases in the last five years (between 2020 and 2024). A total of 69 relevant articles were examined to evaluate the performance of MT, explore persistent challenges and assess the effectiveness of proposed or used solutions. The analysis shows that while NMT has emerged as the predominant approach, its effectiveness is often reduced by the scarcity of training data and the structural complexity of low-resource languages. Strategies such as active learning, data augmentation, multilingual models and transfer learning are identified as critical for improving translation performance. Additionally, emerging research trends, including data pre-processing, optimization of decoder and rule-based approach demonstrate promising directions for addressing existing limitations. In terms of evaluation, most of the studies used Character n-gram F-score (ChrF), Translation Edit Rate (TER), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Word Error Rate (WER) and Bilingual Evaluation Underscore (BLEU) as techniques’ validation metrics. This review provides a detailed evaluation of the current state of MT for low-resource languages and emphasizes the need for further research into underrepresented languages and the development of comprehensive datasets. © 2013 IEEE.
Item Type: | Article |
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Impact Factor: | Cited by: 0 |
Uncontrolled Keywords: | Adversarial machine learning; Neural machine translation; Digital resources; Low resource languages; Machine translation performance; Machine translation technique; Machine translations; Parallel corpora; Performance; Systematic literature review; Computer aided language translation |
Depositing User: | Mr Ahmad Suhairi Mohamed Lazim |
Date Deposited: | 08 Jul 2025 16:42 |
Last Modified: | 08 Jul 2025 16:42 |
URI: | http://scholars.utp.edu.my/id/eprint/38955 |