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Academic Journal of Humanities & Social Sciences, 2023, 6(24); doi: 10.25236/AJHSS.2023.062403.

An Empirical Validation of Domain-Specific English Parallel Corpus for Mechanical Translation Efficacy Enhancement


Yon Jee Kwun (Yang Yikun)1, Yon Jee Eean (Yang Yiyan)2

Corresponding Author:
Yon Jee Kwun (Yang Yikun)

1Foreign Language School, Gannan Normal University, Ganzhou, China

2School of Information Management, Jiangxi University of Finance and Economics, Nanchang, China


This article highlights the pivotal role played by domain-specific English parallel corpus (DSEPC) in Neural Machine Translation (NMT) model training by applying DSEPC that can be utilized to train the model and improve the quality of rendered translations. After empirical study, it turns out that DSEPC can further augment the accuracy and fluency of translations. Therefore, the access to domain-specific corpora is imperative for effective and high-quality NMT model training.


Corpus-based Machine Translation; Domain-specific English Parallel Corpus; Neural Machine Translation; Translation Efficacy

Cite This Paper

Yon Jee Kwun (Yang Yikun), Yon Jee Eean (Yang Yiyan). An Empirical Validation of Domain-Specific English Parallel Corpus for Mechanical Translation Efficacy Enhancement. Academic Journal of Humanities & Social Sciences (2023) Vol. 6, Issue 24: 13-18. https://doi.org/10.25236/AJHSS.2023.062403.


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