## 2 Related Works  
### Traditional Mongolian-Chinese Machine Translation  
Language is part of culture and not only assumes the role of enhancing national unity, strengthening identity, and passing on culture, but it also conveys information about the customs, values, and social attitudes to the outside world. Traditional Mongolian, the official way of writing the Mongolian language in the Inner Mongolia Autonomous Region, is an agglutinative language, and its word formation and morphology are accomplished by linking different affixes to the roots and stems of words, which results in a complex grammatical form and a large vocabulary [30; 31]. However, a high-quality MN-CH parallel corpus is still very scarce, which poses difficulties for machine translation.  
After reviewing a large amount of research, the development trend of MN-CH machine translation is summarized as follows.  
Before 2017, research on MN-CH machine translation focused on example-based machine translation (EBDT), rule-based machine translation (RBMT), and statistical machine translation (SMT), and the research methods during this period mainly included multi-strategy translation systems, SMT systems based on phrases or hierarchical phrases, and MT systems combining the characteristics of Mongolian words. In order to improve the large number of word order errors in Chinese-Mongolian SMT, [PERSON] et al. [32] proposed a Chinese sentence reordering method based on the Mongolian word order. To alleviate the data sparsity and linguistic morphological differences in MN-CH statistical machine translation, [PERSON] et al. [33] proposed a method for constructing a lexeme-based translation model, with Mongolian lexemes as the central language. [PERSON] et al. [34] proposed a multi-method fusion approach to MN-CH machine translation, where they added subword translation monolingual data to attention-based NMT and use SMT to guide the model to produce correct translations. [PERSON] et al. [31] also proposed combining the template-based machine translation (TBMT) system with the SMT system to achieve better translation results.  
In 2017, MN-CH machine translation gradually transitioned to NMT, with NMT methods dominating Mongolian-Chinese machine translation from 2018 to the present. During this period, MN-CH machine translation has presented the following characteristics: pure NMT research dominates and follows the international mainstream deep learning methods closely, and sometimes, SMT methods are used as a supplement to NMT methods. [PERSON] et al. [35] argued that the Transformer model's positional encoding cannot correctly express the deep logical relationships between document sentences, so they proposed a method to improve positional encoding. Additionally, they proposed a method to fuse inter-sentence relationship information to improve the translation quality. [PERSON] et al. improved the generative adversarial network (GAN) by adding value constraints and semantic enhancement, and then they used it to improve the unknown (UNK) words problem in NMT [36].  
Nowadays, the research on MN-CH NMT has absorbed the international advanced research ideas and methods and has made promising progress, but the research directions are more scattered, and there is a lack of research themes with depth, systems, and strong cascading relationships.