Recent advances of low-resource neural machine translation

Haque, Rejwanul, Liu, Chao-Hong and Way, Andy (2021) Recent advances of low-resource neural machine translation. Machine Translation, 35 (4). pp. 451-474. ISSN 1573-0573

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Abstract

In recent years, neural network-based machine translation (MT) approaches have steadily superseded the statistical MT (SMT) methods, and represents the current state-of-the-art in MT research. Neural MT (NMT) is a data-driven end-to-end learning protocol whose training routine usually requires a large amount of parallel data in order to build a reasonable-quality MT system. This is particularly problematic for those language pairs that do not have enough parallel text for training. In order to counter the data sparsity problem of the NMT training, MT researchers have proposed various strategies, e.g. augmenting training data, exploiting training data from other languages, alternative learning strategies that use only monolingual data. This paper presents a survey on recent advances of NMT research from the perspective of low-resource scenarios. © The Author(s), under exclusive licence to Springer Nature B.V. 2021.

Item Type:	Article
Uncontrolled Keywords:	Low-resource machine translation; Neural machine translation; Statistical machine translation
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	12 Jun 2025 15:14
Last Modified:	12 Jun 2025 15:14
URI:	https://norma.ncirl.ie/id/eprint/7835

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