## 1 Introduction
[8] used BERT as a pre-trained model to compare how three different types of languages (English, Korean, and Russian) encode different layers of morphological and grammatical features. The experimental results largely explain that the layers of the model follow the so-called classic NLP pipeline principles, with lower levels specifically processing part-of-speech and other morphological information, middle layers responsible for more complex syntactic relationships, and higher levels dealing with higher-level linguistic phenomena such as anaphora and reference. In 2023, [PERSON] et al. [9] studied the inner workings of mBERT and XLM-R to test the performance of single neural units responding to precise grammatical phenomena (i.e., number agreement) in five languages (English, German, French, Hebrew, and Russian). Cross-language consistency. Experimental results show that there is a large overlap in the underlying dimensions of encoding consistency in these languages and that the overlap in XLM-R short-range consistency is larger than that of mBERT and peaks in the middle layers of the network. The Uighur Mongolian script, which has a history of about 800 years, is the first script mastered by the Mongolian people. However, Mongolian-Chinese machine translation (MCMT) started late, and the parallel corpora are limited. The cutting-edge research on this topic is relatively weak, so there is still great room for improvement. In response to the scarcity of parallel corpus resources in MCMT, in 2021, [PERSON] et al. [10] modeled machine translation by introducing three language pre-training models. In the data pre-processing stage, they introduced two new unsupervised pre-training methods and one supervised pre-training method for cross-border Language modeling is used to learn cross-language representations, and the effects of three language pre-training methods in Mongolian-Chinese translation are studied. Experimental results show that the three cross-language pre-trained models significantly reduce the perplexity of low-resource languages and increase the BLEU value by 20.4 compared with random initialization parameters, improving the quality of Mongolian-Chinese translation. In 2022,He and [PERSON] [11] took full advantage of agglutination and rich morphological changes in Mongolian, as well as Mongolian grammar rules, to build a neural word segmentation method in which the iLSTM-CNN-CRF neural network was used to filter the Mongolian connecting vowels and unstable \"N\" as stop words to improve the quality of machine translation, and the BLEU (BiLingual Evaluation Understudy) value in their obtained translations reached 73.30%. Mongolian is also the same as other languages. Each region has its own dialect characteristics. Approximately 80% of Mongolia is Khalkha Mongolian, so the Mandarin in Outer Mongolia is Khalkha Mongolian. On this basis, many Russian and English words are borrowed. The situation in Inner Mongolia is much more complicated, and the accents between the East and the West are quite different. But generally speaking, their mother tongue is Mongolian (Cyrillic Mongolian: Monional X31), which belongs to the Mongolian family of the Altaic language family. It is a pinyin script. Its two writing methods--horizontal writing and vertical writing--do not affect reading. This article is written in Mongolian horizontally. So far, researchers have made gratifying progress in the study of Mongolian-Chinese machine translation. However, the research conditions are limited, and the construction of the Mongolian-Chinese parallel corpus is not perfect, resulting in a large number of low-frequency words. The traditional Mongolian word formation method is complex, and the direct use of neural networks for translation has poor results.