## 4 Experiments and Analysis
### Experimental Results and Analysis  
#### 4.5.1 Comparative Tests  
In this paper, the symbol of the baseline model was defined as Transformer_Base, and that of the proposed MCNMT model was defined as Transformer_Temp_Kno. To reduce the uncertainty due to a single test, three tests were performed on the conditions of the random seeds of \(S_{1}=125\), \(S_{2}=1234\), \(S_{3}=4096\), as well as the corresponding initialization network parameters. The number of training rounds per session Epoch = 40.  
The training times for the Transformer_Base and Transformer_Temp_Kno are listed in Table 3:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Total Sentence** & **Training Set** & **Validation Set** & **Testing Set** \\ \hline
1,625,697 & 605,697 & 10,000 & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset division.  
Figure 12: Chinese word segmentation results through BPE. Chinese is divided into single characters. The sentence meanings in the figure are “The streets are very lively now.”, “I’ll rest at your house for a while before leaving.”, “The environment in which people come to survive.”, and “Please check if the program exists.” respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \
\begin{tabular}{c} **Total Parameter** \\ **Number** \\ \end{tabular} & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) &
\begin{tabular}{c} **Average** \\ **Time (Min)** \\ \end{tabular} \\ \hline Transformer\_Base & 48,791,375 & 1536 & 1531 & 1518 & 1528.3 \\ Transformer\_Temp\_Kno & 49,451,601 & 12,078 & 2086 & 2072 & 2076.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model parameters and training time.  
The changes of BLEU values for each model are shown in Table 4:  
As can be seen from Table 4, compared with the baseline model, our proposed Transformer_Temp_Kno model has a higher BLEU value, with an increase of 4.032. Although the training speed of our model has decreased, its overall translation quality and stability have been improved to a certain extent.  
Figures 13 and 14 show the loss function curves and accuracy curves of the Transformer_Temp_Kno model on the training and validation sets, respectively, which were obtained with the aforementioned three seeds.  
Table 5 presents the comparison of several translation examples obtained through testing on the test set with the random seed of \(5_{3}\).  
From each translation example in Table 5, due to the limited scale of the Mongolian-Chinese parallel corpus, the baseline model often has the problem of insufficient grammatical information in the translation process, and when translating some long sentences, it can only translate word by word, resulting in the semantics of the translated Chinese script. The integration of soft target templates and contextual knowledge into the model can help the model learn semantic knowledge and guide the translation so that the translated text is richer and more fluent.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**BLEU Value**} & \multirow{2}{*}{**Average Time**} \\ \cline{2-2} \cline{4-5}  & \(\mathcal{S}_{1}\) & \(\mathcal{S}_{2}\) & \(\mathcal{S}_{3}\) & **(Min)** \\ \hline Transformer\_Base & 39.735 & 39.614 & 39.153 & 39.501 \\ Transformer\_Temp\_Kno & 43.593 & 43.144 & 43.861 & 43.533 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of BLEU values of different models.  
Figure 14: Accuracy curves of the Transformer_Temp_Kno model with various seeds on the training set and validation set.  
Figure 13: Loss function curves of the Transformer_Temp_Kno model with various seeds on the training set and validation set.