# The Task of Post-Editing Machine Translation for the Low-Resource Language
## 3 Results
### Dataset
The parallel corpus for the English-Kazakh language pair was collected from various official websites, aligned, and tokenized. The translation of the source {src} English corpus was implemented using machine translation systems such as Prompt, Yandex, and Webtran. Figure 6 illustrates the OpenNMT library set for training and deploying neural machine translation models on example of sentence \"O.i.c.a. Translation and text editing are depicted in Figure 7. In the first cell on the top left is the original text in English, in the cell opposite is its translation into the Kazakh language, and in the last cell the results of the post editing are shown. Additionally, testing was performed on translated texts containing complex structures and numerous abbreviations. One of the first metrics that demonstrated a high correlation with human quality evaluations is BLEU. This metric still remains one of the most popular and widely used metrics. WER (Word Error Rate) is a general measure of system performance, and TER (Translation Error Rate) is a method used to predict the volume of post-editing for machine translation outputs. BLEU (Bilingual Evaluation Understudy) is one of the most common automatic metrics for assessing the quality of machine translation. It is based on measuring the overlap between the sets of words in the generated translation and the reference translation. Following are some reasons why BLEU may be considered a better metric in some cases:  
Simplicity and clarity: BLEU is easy to understand and calculate. This is one of the reasons for its popularity. Its formula involves simple operations such as counting sets of words and their intersections. Consistency with human assessments: In some studies, BLEU has shown good agreement with human quality assessments. This does not mean that BLEU always accurately reflects human evaluation, but in some cases, it provides a good correlation. Easy interpretation: BLEU provides a numerical value that is easy to interpret. A high BLEU often means a translation that is more accurate and similar to the reference. Applicability to various tasks: BLEU can be used to evaluate the quality of translation in various fields and languages, making it universal and applicable to a wide range of tasks. The results of the experiments with the trained post-editing model for English-Kazakh and Russian-Kazakh machine translation outputs are presented in Table 4, and for the Uzbek language, they are presented in Table 5.  
The technologies and approaches proposed in the article for post-editing machine translation of the Kazakh and Uzbek languages have many advantages and usefulness and can also be effective in the context of improving the quality of translation. The developed modules provide linguistic accuracy and identification of types of complex sentences, consider the syntactic and semantic properties, which are important for complex language structures like Kazakh and Uzbek, and provide translation of terms and phrases. Since the hybrid approach benefits from an automatic editor plus additional editing modules, such as spelling errors, common nouns, and terms, productivity in the post-editing process is  
Figure 7: Example of English–Kazakh translation and post-editing of the system. significantly increased. The developed approaches allow us to achieve a more accurate and high-quality edited text. Thus, light and full editing approaches for post-editing machine translation are useful and effective tools that can significantly improve translation quality, increase productivity, and reduce costs. To achieve better translation results, it is necessary to have a more extensive, well-structured, clean, and diverse corpus. Our corpus at this stage covers specific topics and was created using forward translation (FT) and backward translation (BT) methods. The translated texts generated by the BRNN models were improved using the transformer post-editing model, demonstrating its effectiveness in the considered post-editing task.