## 4 Experiments and Analysis
### Data Pre-Processing
#### 4.3.2 Word Segmentation  
The NMT model is limited by objective conditions such as video memory capacity during training, and its model size is usually controlled. Hyperparameters directly related to the model scale include word embedding dimensions, sentence length, corpus dictionary size, the constituent units and number of layers of the encoder and decoder, etc. Take a dictionary as an example. Before construction, a smaller frequency of occurrence of a word is usually set as a limit. Words with a frequency lower than the limit are marked with UNK. The size of the dictionary is generally limited to about 30,000 to 50,000 words. Between them, the translation model cannot recognize the \"UNK\" information during the training process. If unregistered words--words that occur less frequently--are entered during the testing or validation phase of the model; the model cannot solve this problem. What follows is the difficulty of the NMT model in processing these words, which is reflected in the fact that words marked as UNK cannot participate in model training, which reduces the ability of the NMT model to completely capture the semantic information of the source language and target language, and if certain sentences The first half is the same, but the second half is replaced by UNK, which will also cause ambiguity. The main solutions are: 1. Use an external dictionary or expand the vocabulary. 2. Use fine-grained grammatical units at the character (Character) level and sub-word (Sub-word) level.  
The morphological form of Mongolian is similar to that of English. There are roots and affixes, and the same root with different affixes can form words with different meanings, which leads to the rare Mongolian words. Therefore, this article uses the BPE-Dropout algorithm to perform fine-grained segmentation of the Mongolian-Chinese bilingual parallel corpus. In 2016, [PERSON] et al. [16] introduced the BPE algorithm into the field of natural language processing to effectively alleviate the problem of unregistered words. The Algorithm 1 [16] process is as follows:  
```
Input: corpus data Output: Multi granularity fusion dictionary V\({}_{\text{BPE}}\) Translate corpus data into the most basic characters Count the frequency of occurrence of the characters For MON (merge operand): Merge the character pairs with the highest frequency of occurrence, denoted as Symbol\({}_{\text{com}}\) Update multi granularity fusion dictionary, \(\text{V}_{\text{BPE}}\cup\text{Symbol}_{\text{com}}\) End Return Multi granularity fusion dictionary V\({}_{\text{BPE}}\)
```  
**Algorithm 1** BPE word segmentation  
In this algorithm, only the unique hyperparameter, the merge operand, is controlled. The size of the vocabulary list can be controlled by artificially setting the merge operand. In this work, the merge operand is set to 20,000. Words are separated by using @@, the Mongolian text is divided into words or subwords with different granularities; and the Chinese text is segmented into single words.  
Examples of Mongolian-Chinese bilingual word segmentation are shown in Figures 11 and 12 respectively:  
Figure 11: Mongolian word segmentation results through BPE. Mongolian is divided into syllables and phrases. The sentence meanings in the figure are “The streets are very lively now.”, “_T_ll rest atyour house for a while before leaving.\", \"The environment in which people come to survive.\", and \"Please check if the program exists.\" respectively.  
After word segmentation, the Mongolian-Chinese parallel corpus still has a total of 625,697 sentence pairs.