Research on English Translation System Combining Statistical Machine Translation and Deep Learning
Journal: Region - Educational Research and Reviews DOI: 10.32629/rerr.v7i4.3864
Abstract
With the rapid development of machine translation technology, Statistical Machine Translation (SMT) and
Neural Machine Translation (NMT) have become the two dominant translation methods. In recent years, hybrid models
combining these two methods have garnered increasing attention due to their ability to integrate the advantages of both
approaches, thereby improving translation quality. This paper compares the translation performance of three models (SMT, NMT, and hybrid models), designs a series of experiments, and provides a detailed analysis of the experimental results. The results indicate that the hybrid model outperforms both the SMT and NMT models in terms of BLEU score, TER score, and human evaluation, proving the effectiveness of combining statistical machine translation with deep learning. The
research in this paper provides new ideas and methods for the further development of machine translation technology.
Keywords
statistical machine translation, deep learning, neural machine translation, hybrid model
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for English → Mizo Language. SN Computer Science. 2023; 4(6): 15-19.
[2] Liu Pengjuan. Research on Offline Model Training for Large-Scale Distributed Statistical Machine Translation. Automation and Instrumentation. 2023(12): 18-22.
[3] Xu Hong, Huang Xiean. A Review of Pre-Editing and Machine Translation Research (1990—2023). Foreign
Language Audio-Visual Teaching. 2023(06): 43-49+112.
[4] Harrydanmu Abdukirim, Hou Yutao, Yao Dengfeng, et al. A Review of Uyghur Language Machine Translation
Research. Computer Engineering. 2024; 50(01): 1-16.
[5] He Yuanyuan. Research on Improving the Quality of Cross-Border Tourism Translation through Statistical
Machine Translation. Automation and Instrumentation. 2023(09): 201-204.
[6] He Chenghao, Wang Zehui, Teng Junzhe, et al. A Review of Machine Translation. Computer Knowledge and
Technology. 2023; 19(21): 31-34. [7] Xie Gengquan, He Junlin. A Brief Discussion on the Chinese-English Machine Translation Model Based on
Kernel Ridge Regression Technology. Journal of Heihe University. 2022; 13(12):181-183.
[8] Lu Chen, Luo Guihua. Comparative Analysis and Statistics of Errors in Machine Translation of Prose. Journal of
Luoyang Normal University. 2022; 41(12): 73-78.
[9] Li Zheng. Application Analysis of Statistical Machine Translation Based on Neural Network Language Model. Information and Computer (Theoretical Edition). 2022; 34(22): 109-111.
[10] Yang Yingying. (2022) Application of Machine Translation in RS10 Cloud Platform Products.[D]General
Research Institute for Mechanical Science, Beijing.
[11] Yang L ,Lin Y .Constructing a University English Translation Course Teaching System Based on POA and
MVETC. Open Journal of Modern Linguistics. 2024; 14(06):1140-1158.
[12] Antonios B B ,Andemariam W S ,Asfaha M Y .Pluralistic language policy and multilingual legal texts in Eritrea. Journal of Multilingual and Multicultural Development. 2024; 45(10):4072-4085.
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