Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 3
799-804,810

XLNet-Transformer optimization method for Chinese-Malay low-resource neural machine translation based on deep coded attention

Zhan Siqia
Xu Zhizhana
Yang Weib
Xie Qianglaib
a. College of Information Engineering, b. Big Data Laboratory of Collaborative Innovation Center, Jiangxi University of Technology, Nanchang 330098, China
DOI: 10.19734/j.issn.1001-3695.2023.08.0331

Abstract

Neural machine translation(NMT) has achieved remarkable results in applications in many fields, and it has fully demonstrated its superiority on large-scale corpora. However, there is still a huge room for improvement when there are insufficient corpus resources. The lack of a Chinese-Malay parallel corpus directly affects the translation effect of Chinese-Malay machine translation. In order to solve the problem of unsatisfactory Chinese-Malay low-resource machine translation, this paper proposed a low-resource neural machine translation method based on deep encoded attention and progressive unfreezing. Firstly, this method reconstructed the encoder using the XLNet pre-training model and replaced the output mode of the traditional encoding layer with the XLNet dynamic aggregation module in order to effectively compensate for the bottleneck caused by the lack of Chinese-Malay corpus. Secondly, it improved the traditional encoding-decoding attention by using a parallel crossattention module in the decoder, which enhanced the ability to capture the potential relationship between the source word and the target word. Finally, it adopted a progressive unfreezing training strategy to maximize the release of the model's perfor-mance. The experimental results demonstrate that the proposed method significantly improves the performance on a small-scale Chinese-Malay dataset, thus confirming its effectiveness. Compared with other low-resource NMT methods, this method had a simpler structure, and improved the encoder and decode, resulting in a more significant enhancement in the translation effect. The approach provides effective strategies and insights to cope with low-resource machine translation.

Foundation Support

江西省教育厅科学技术研究资助项目(GJJ2202613,GJJ212015)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.08.0331
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 3
Section: Algorithm Research & Explore
Pages: 799-804,810
Serial Number: 1001-3695(2024)03-022-0799-06

Publish History

[2023-10-12] Accepted Paper
[2024-03-05] Printed Article

Cite This Article

占思琦, 徐志展, 杨威, 等. 基于深度编码注意力的XLNet-Transformer汉-马低资源神经机器翻译优化方法 [J]. 计算机应用研究, 2024, 41 (3): 799-804,810. (Zhan Siqi, Xu Zhizhan, Yang Wei, et al. XLNet-Transformer optimization method for Chinese-Malay low-resource neural machine translation based on deep coded attention [J]. Application Research of Computers, 2024, 41 (3): 799-804,810. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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