## 1 Introduction  
Translation, as a bridge and link between different countries and national cultures, is of great importance. With the economic and social development of the Inner Mongolia Autonomous Region of China and the increasingly close exchanges and cooperation between Mongolia and China, human translation can no longer meet the needs of massively accurate translation. Therefore, machine translation, with its excellent translation quality and speed, has gradually become the mainstream of current information transmission, which has considerable social and practical significance for improving the dissemination and exchange of national culture and breaking down different cultural barriers. With the advent of the world's first electronic digital computer, the ENIAC (Electronic Numerical Integrator and Computer), [PERSON] first proposed the idea of document translation through computers in 1947 and officially proposed the concept of machine translation in 1949 [1]. In 2003, Universite de Montreal's [PERSON] et al. [2] used neural network models to improve the traditional n-gram model and the statistical language model to develop a new language model that can utilize longer contexts to achieve better results in the text corpus. In 2017, [PERSON] et al. [3] proposed a constraint-based bilingual vocabulary induction method in view of the lack of parallel corpora and comparable corpora. This method extends the constraints of recent fulcrum-based induction techniques and further enables multiple Symmetry assumption loops to reach more cognate words in the transgraph. Compared with previous methods, the constraint-based method proposed in this paper has a statistically significant improvement in accuracy and f-score. The results show that this approach has the potential to complement other bilingual dictionary creation methods. In 2020, the Google team released the Reformer model on ICLR [4], which reduces the spatial complexity by using Locality-Sensitive Hashing (LSH), blocks the feed-forward model of Transformers with a reversible residual network (RevNet), and solves the problem that the memory cost of the residual network is proportional to the number of network units in this model. In 2022, [PERSON] et al. [5] presented a template-based approach to regenerate constraint and non-constrained tokens through templates. The generation and derivation of templates can be learned through a sequence-to-sequence training framework to produce high-quality translations while maintaining decoding speed. In 2022, [PERSON] et al. [6] utilized an external parser to parse the source sentence to obtain dependency parsing data, then converted the dependency parsing data into a parent word position vector and a child word weight matrix, and finally integrated the dependency knowledge into the multi-head attention mechanism of the Transformer encoder. Their experimental results indicated that the bidirectional self-attention mechanism can provide richer dependency information to effectively improve the translation performance of the model. Integrating linguistic information into neural machine translation models and using existing linguistic knowledge to alleviate the inherent difficulties faced by neural machine translation and improve translation quality has become a hot topic in the field of neural machine translation research. In 2022, [PERSON] et al. [7] studied the ability of the multilingual BERT (mBERT) language model to transfer syntactic knowledge across languages, using structural probes to reconstruct the dependency parse tree of sentences and using context embeddings from the mBERT layer to represent the input sentences. The results of the experimental evaluation show that the grammatical knowledge of the mBERT model can be transferred between languages. Transferring grammatical knowledge not only meets theoretical needs in the case of specific phenomena but also has important practical significance in syntactic tasks (such as dependency parsing). In the same year, [PERSON] et al.