# The Task of Post-Editing Machine Translation for the Low-Resource Language
## 1 Introduction
### Review of Research Studies  
A comprehensive review of the development of post-editing in machine translation (MT) was provided by the authors [7; 8]. They describe that initially, post-editing was conceived as a final step in the MT process, and the post-editor did not need to have knowledge of both languages; they could edit the text knowing only the language of translation. Post-editing is increasingly carried out by professional translators, as it is now a service for clients with its international standard [9], which requires post-editors to have expertise and professional experience. Modern methods of text post-editing were discussed in the work [10]. The authors [11] examined post-editing in neural machine translation (NMT). Neural machine translation allows for achieving significant quality compared to other machine translation systems (MTSs). The results of neural machine translation are perceived as smoother and more natural than those of statistical machine translation (SMT). Therefore, it can be concluded that NMT should enhance post-editing efficiency to a greater extent compared to previously used approaches. The use of neural machine translation is significantly growing in application development. Many studies show that, overall, NMT results can achieve higher scores compared to the performance of other types of MTSs. In both research and practical applications of NMT in professional settings, reviews and comparative analyses of NMT and SMT have been conducted regarding quality. In a study [12], it was found that the English-Spanish (EN-ES) NMT system outperformed the SMT system in a study incorporating training data from 15 million words. The authors noted issues with NMT, including domain mismatch and the use of rare words. Generally, when NMT has access to sufficiently large training data, matching the domain and vocabulary of the texts to be translated, there is a substantial improvement in translation quality. High-quality NMT systems make fewer errors, reducing the workload for automatic post-editing (APE) systems. Given the dominance of the NMT approach in both academic and industrial applications, significant attention to this approach is highly justified. The development of machine translation for the Kazakh language began in 2000. Professor [PERSON] was one of the pioneers in this field, establishing a research school actively engaged in MT studies. Through the work of the research group under Professor [PERSON]'s guidance, grammatical formal models of simple sentences and the first version of a machine translation program (MTP) from Kazakh to English were defined [13; 14]. Additionally, a polysemous method for machine translation of morphologically complex natural languages such as Russian and Kazakh was developed [15; 16; 17]. Since 2018, efforts have been ongoing to develop and research a neural machine translation system for the Kazakh language. Over the last three to four years, both the theory and practice of machine translation have significantly expanded, creating a new direction in machine translation that reflects very high quality. This, in turn, has raised the standard of machine translation quality to new heights. 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 post-editing of machine translation began in 2016. For example, in the study [19], an automatic post-editing system for the English-Italian language pair based on a bidirectional recurrent neural network model is introduced. In this classical approach, the model consists of an encoder, which encodes the MT output into a fixed-length vector, from which the decoder ensures transformation post-editing. With continuous research, model architectures have become more complex, and results have improved compared to the previous architecture.