# The Task of Post-Editing Machine Translation for the Low-Resource Language
## 3 Results
### Dataset
Considering the importance of understanding and translating abbreviations and simple shortenings, a module was developed to address abbreviation editing issues for the English-Kazakh and Russian-Kazakh language pairs; a description is provided in Table 3.  
To develop the post-edit module for the English-Kazakh and Russian-Kazakh language pairs, a machine learning-based approach was adopted. Parallel corpora were necessary for experiments and testing. English-Kazakh parallel corpora were collected from open sources such as the OPUS project and WMT conferences [36, 37] and by parsing websites containing content in multiple languages. The Bitextor open-source application was used for parsing, applying a set of heuristics to select file pairs containing the same text in two different languages. Bitextor was applied to various websites, including the Kazakh National University ([[http://www.kaznu.kz](http://www.kaznu.kz)]([http://www.kaznu.kz](http://www.kaznu.kz))) (accessed on 1 April 2023), Bolashak International Scholarship bolashak.gov ([[http://www.bolashak.gov.kz](http://www.bolashak.gov.kz)]([http://www.bolashak.gov.kz](http://www.bolashak.gov.kz))) (accessed on 29 April 2023), Eurasian National University ([[http://www.enu.kz](http://www.enu.kz)]([http://www.enu.kz](http://www.enu.kz))) (accessed on 13 June 2023), KazzPost ([[http://www.kazpost.kz](http://www.kazpost.kz)]([http://www.kazpost.kz](http://www.kazpost.kz))) (accessed on 19 May 2023), news portals ([[http://inform.kz](http://inform.kz)]([http://inform.kz](http://inform.kz)), [[http://tengrinews.kz](http://tengrinews.kz)]([http://tengrinews.kz](http://tengrinews.kz))), Legal information system of Regulatory Legal Acts of the Republic of Kazakhstan ([[https://adilet.zan.kz](https://adilet.zan.kz)]([https://adilet.zan.kz](https://adilet.zan.kz))) (accessed on 2 September 2023) and others. A total of 45,000 English-Kazakh sentence pairs were obtained. For training the Russian-Kazakh and English-Kazakh translation model, a \"Treebank\" corpus (parallel sentences aligned in three languages--Kazakh, Russian, and English) was  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Text with Errors in Proper Names** & **Test for Editing Proper Names** & **Translation from English** \\ \hline Kaakakstanamamathakaama  
used, collected, and aligned through various tools. The total volume of the corpus is 680,000 sentences. The model training data consisted of 90% of the corpus, 5% for validation, and 5% for testing. The vocabulary was created from frequently used words in the corpora (occurring more than three times) and consisted of 120,000 words. To create the prototype of the post-editing system, neural network models such as BRNN and transformer were utilized. These models were configured based on the user-friendly OpenNMT v2.0.0 ([[https://github.com/OpenNMT/OpenNMT-py/releases/tag/2.0.0](https://github.com/OpenNMT/OpenNMT-py/releases/tag/2.0.0) rc1]([https://github.com/OpenNMT/OpenNMT-py/releases/tag/2.0.0](https://github.com/OpenNMT/OpenNMT-py/releases/tag/2.0.0) rc1)) (accessed on 1 September 2023) application, which employs the PyTorch machine learning framework. Experiments were conducted on a test corpus with a volume of 380,000 sentences. In the first stage, English-Kazakh or Russian-Kazakh neural machine translation was performed using the BRNN model. Subsequently, the transformer model was used for post-editing the text. Afterward, complex structural and grammatical forms were processed, and abbreviations were replaced. Training MT models: OpenNMT [38] is a deep learning framework specializing in sequence-to-sequence models, covering various tasks related to machine translation, speech recognition, image-to-text conversion, and more. Training corpora for post-editing were implemented using OpenNMT. In the experiment under consideration, post-editing for the English-Kazakh language pair was explored. A total of 45,000 English-Kazakh parallel sentences obtained from open sources and using the Bitextor application were utilized in this experiment.