# The Task of Post-Editing Machine Translation for the Low-Resource Language
## 2 Materials and Methods
### Development of the Light Post-Editing Level  
Light post-editing is one of the initial and superficial stages of editing in the machine translation post-editing system. The light post-editing level involves taking the raw output from machine translation and making minimal changes to the text to make the translation understandable, essentially accurate, and grammatically correct [30; 31].  
The tasks included in the light post-editing level are as follows:  
Figure 2: Architecture of the machine translation post-editing system for a low-resource language.  
* Correcting only the most obvious typos and lexical and grammatical errors;
* Rectifying machine errors;
* Removing unnecessary or redundant translation options generated by the machine.  
The complete system of endings for the Kazakh language was proposed by Professor [PERSON] in the work [27], and the authors introduced the CSE model (Complete Set of Endings). The CSE system of endings for the Kazakh language was developed using a combinatorial model, taking into account semantic and morphological exceptions. A list of possible endings for all parts of speech was compiled. The system of endings was derived through a combinatorial approach, considering the semantic admissibility of placing the language's basic suffixes and the peculiarities of sound harmonization in the language. All possible types of endings for the Kazakh language are presented, totaling 4727 types of endings.  
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. The system of endings was employed in the post-editing task of the Kazakh language in machine translation. The system of endings was used to correct morphological errors in post-edited Kazakh text.  
To build the light post-editing level model, the following algorithm was developed:  
1. Aligning sentences [mt] obtained from machine translation with high-quality sentences in the target language [pe];
2. Conducting sentence tokenization;
3. Splitting into training and validation sets, as well as building a vocabulary;
4. Processed data are fed into a recurrent neural network;
5. The trained model is used to translate the test corpus, and the results are compared using the BLEU metric [mt]-[pe] and [mt]-[ape], where ape represents the text corpus obtained after the post-editing stage.  
The stages of the light post-editing process are illustrated in Figure 3.  
Certainly, light post-editing is the process of correcting machine translation with minimal changes to achieve an acceptable level of quality. The light post-editing level performs the following functions:  
* Elimination of glaring grammatical, orthographic, and punctuation errors;
* Correction of machine-translated phrases or words that clearly do not match the original text;
* Preservation of the basic formatting and structure of the original text without additional effort on high-quality formatting;  
Figure 3: Stages of the light post-editing algorithm.  
* In the case of extremely complex or ambiguous translations, it may decide to skip or make minimal changes to maintain the text's clarity.  
Ultimately, light post-editing ensures readability and a structured text, preserves overall clarity and understanding of the text, avoids extensive revisions of complex sections, provides minimal grammatical correctness of the text, and corrects obvious translation errors while maintaining the overall meaning. Full post-editing is used for further editing.