The Development and Construction of Bilingual Machine Translation Auxiliary Tool between Chinese and Kazakh Languages

Niyazbek, Muheyat; Talp, Kuenssaule; Sun, Jing

doi:10.1088/1755-1315/687/1/012205

Cited by 4 publications

(1 citation statement)

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“…This study proved the methods used worked; however, they completely relied on the rule-based approach. Niyazbek, Talp & Sun (2021) used the rule-based dictionary method for Chinese–Kazakh (CH–KZ) MT. So many research works related to MT of the Kazakh language are restricted by the rule-based method leaving the NMT approach open to development.…”

Section: Introductionmentioning

confidence: 99%

The neural machine translation models for the low-resource Kazakh–English language pair

Karyukin

Rakhimova

Karibayeva

et al. 2023

PeerJ Computer Science

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The development of the machine translation field was driven by people’s need to communicate with each other globally by automatically translating words, sentences, and texts from one language into another. The neural machine translation approach has become one of the most significant in recent years. This approach requires large parallel corpora not available for low-resource languages, such as the Kazakh language, which makes it difficult to achieve the high performance of the neural machine translation models. This article explores the existing methods for dealing with low-resource languages by artificially increasing the size of the corpora and improving the performance of the Kazakh–English machine translation models. These methods are called forward translation, backward translation, and transfer learning. Then the Sequence-to-Sequence (recurrent neural network and bidirectional recurrent neural network) and Transformer neural machine translation architectures with their features and specifications are concerned for conducting experiments in training models on parallel corpora. The experimental part focuses on building translation models for the high-quality translation of formal social, political, and scientific texts with the synthetic parallel sentences from existing monolingual data in the Kazakh language using the forward translation approach and combining them with the parallel corpora parsed from the official government websites. The total corpora of 380,000 parallel Kazakh–English sentences are trained on the recurrent neural network, bidirectional recurrent neural network, and Transformer models of the OpenNMT framework. The quality of the trained model is evaluated with the BLEU, WER, and TER metrics. Moreover, the sample translations were also analyzed. The RNN and BRNN models showed a more precise translation than the Transformer model. The Byte-Pair Encoding tokenization technique showed better metrics scores and translation than the word tokenization technique. The Bidirectional recurrent neural network with the Byte-Pair Encoding technique showed the best performance with 0.49 BLEU, 0.51 WER, and 0.45 TER.

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Section: Introductionmentioning

confidence: 99%

The neural machine translation models for the low-resource Kazakh–English language pair

Karyukin

Rakhimova

Karibayeva

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

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Research on the Design Method of Auxiliary Translation Tools Proofreading Subsystem Based on Web TM

Zhao

2023

2023 International Conference on Educational Knowledge and Informatization (EKI)

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Research on Intelligent Construction of Bilingual Translation Corpus Based on Differential Evolution Algorithm

2023

Proceedings of the 2023 8th International Conference on Multimedia Systems and Signal Processing

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The Development and Construction of Bilingual Machine Translation Auxiliary Tool between Chinese and Kazakh Languages

Cited by 4 publications

References 0 publications

The neural machine translation models for the low-resource Kazakh–English language pair

The neural machine translation models for the low-resource Kazakh–English language pair

Research on the Design Method of Auxiliary Translation Tools Proofreading Subsystem Based on Web TM

Research on Intelligent Construction of Bilingual Translation Corpus Based on Differential Evolution Algorithm

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