The Solution of the Problem of Unknown Words Under Neural Machine Translation of the Kazakh Language

Turganbayeva, Aliya; Tukeyev, Ualsher

doi:10.1007/978-981-15-3380-8_28

Cited by 4 publications

(4 citation statements)

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“…Neural systems MTPs have significantly enhanced the capabilities of post-editing for the Kazakh language, as deep learning-based models demonstrated superior performance with agglutinative languages [18]. Research into the neural approach for automatic postediting of machine translation began in 2016.…”

Section: Review Of Research Studiesmentioning

confidence: 99%

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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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Section: Review Of Research Studiesmentioning

confidence: 99%

“…The Kazakh language's system of endings was also applied to the segmentation task [18]. The accuracy of converting words to their bases was above 87% for four different texts in the subject area.…”

Section: Development Of the Light Post-editing Levelmentioning

confidence: 99%

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

Rakhimova,

Karibayeva,

Turarbek

2024

Applied Sciences

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“…The technology for solving the problem of unknown words in neural machine translation (NMT) is described in (Turganbayeva and et., 2020). The unknown words are replaced with their synonyms in the dictionary.…”

Section: Related Workmentioning

confidence: 99%

The Translation Quality Problems of Machine Translation Systems for the Kazakh Language

Turarbek

2021

JMMCS

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Kazakh language is related to languages with complex morphology and syntax. Today, most machine translation systems consider Kazakh language too, like Google, Yandex, Prompt, etc. This article describes the errors, shortcomings, and problems of machine translation (MT) into the Kazakh language. To analyze errors in machine translation into the Kazakh language, the most popular electronic translation programs were selected. When translating from Russian and English into Kazakh (and vice versa), various errors may occur, since the Kazakh language is different from other languages, and has special characteristics. To compare the results, an empirical method was used, namely, monitoring and testing the translation results of machine translation systems. Considering the results of statistical methods, the rule-based and neural networks based methods in machine translations were also analyzed. The practical significance of the study lies in the development of recommendations for the identification and elimination of errors when editing the results of MT. The scientific significance of the study lies in the fact that for the first time errors and inaccuracies arising from machine translation of the Kazakh language have been systematized. The assessment of the quality of MT is also presented. The research carried out in this article will be used for the post-editing problem in machine translation.

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“…The main scientific contribution of this work is the development of an algorithm for converting an out-of-domain word into an in-domain word by finding an unknown word in the dictionary of the trained model, which is replaced by a word from the synonym dictionary that is close in meaning. This paper is an expanded version of work (Turganbayeva & Tukeyev, 2020). For this work, additional experiments were carried out and an addition was made for each section.…”

Section: Introductionmentioning

confidence: 99%