Semantic Connections in the Complex Sentences for Post-Editing Machine Translation in the Kazakh Language

Turganbayeva, Aliya; Rakhimova, Diana; Karyukin, Vladislav; Karibayeva, Aidana; Turarbek, Asem

doi:10.3390/info13090411

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…The BLEU score with the BPE segmentation reached the values of 0.23 and 0.22. In Turganbayeva et al (2022) , the grammatical structure of complex Kazakh sentences was observed. This study proved the methods used worked; however, they completely relied on the rule-based approach.…”

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

Self Cite

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“…This complexity poses challenges for machine translation, as it is necessary to correctly interpret and translate each morpheme. Translation between Kazakh and languages with different morpho-logical structures, such as English, can lead to a lack or excess of information in the target text [25].…”

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confidence: 99%

“…In the software implementation, post-editing is carried out using both light and full post-editing modules and a rule-based approach. This approach allows for working with both simple and complex sentences [25].…”

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confidence: 99%

The Task of Post-Editing Machine Translation for the Low-Resource Language

Rakhimova,

Karibayeva,

Turarbek

2024

Applied Sciences

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In recent years, machine translation has made significant advancements; however, its effectiveness can vary widely depending on the language pair. Languages with limited resources, such as Kazakh, Uzbek, Kalmyk, Tatar, and others, often encounter challenges in achieving high-quality machine translations. Kazakh is an agglutinative language with complex morphology, making it a low-resource language. This article addresses the task of post-editing machine translation for the Kazakh language. The research begins by discussing the history and evolution of machine translation and how it has developed to meet the unique needs of languages with limited resources. The research resulted in the development of a machine translation post-editing system. The system utilizes modern machine learning methods, starting with neural machine translation using the BRNN model in the initial post-editing stage. Subsequently, the transformer model is applied to further edit the text. Complex structural and grammatical forms are processed, and abbreviations are replaced. Practical experiments were conducted on various texts: news publications, legislative documents, IT sphere, etc. This article serves as a valuable resource for researchers and practitioners in the field of machine translation, shedding light on effective post-editing strategies to enhance translation quality, particularly in scenarios involving languages with limited resources such as Kazakh and Uzbek. The obtained results were tested and evaluated using specialized metrics—BLEU, TER, and WER.

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