Joint NMT models for text conversion between traditional Mongolian script and cyrillic Mongolian: a comparative study

The research aims to develop a high-precision machine learning model for translating between Mongolian and Cyrillic scripts. Although Mongolian and Cyrillic scripts are different scripts of the Mongolian language, their sentence structure, number of words in a sentence, and position are the same. This article presents the results of our research on neural machine translation models tested for translating between the two scripts. The Seq2Seq and Transformer models with attention mechanisms were trained using each tokenization method at the character, subword, and word levels. The accomplishments of these models were assessed regarding the conversion quality, computational efficiency, and the ability to handle the unique characteristics of the two scripts, and the benefits and drawbacks of each model were summarized. It is believed that this comparison helps select the most suitable NMT model for similar tasks. The developed model has a vast potential for development and application as a system for translating between scripts, and it will facilitate some clerical work in public and private sector organizations at all levels providing services in Cyrillic and Mongolian scripts.