## 6 Integration of Primer-EZ Methods Based on Improved Models
### Experimental Results and Discussion  
The experimental conditions in this section are the same as in Section 5.3.2, where N = 5, epochs = 35, and the average BLEU value results are shown in Table 6. Table 7 shows a comparison of the results of all the experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **100** & **6421** & **2169** & **500** & **256** & **Avg. BLEU** \\ \hline Transformer\_Base + EZ (Squared Leaky ReLU) & 37.275 & 37.607 & 37.135 & 37.409 & 37.021 & 37.289 \\ Transformer\_Enc\_Imp + EZ (Squared Leaky ReLU) & 43.866 & 44.795 & 45.236 & 45.251 & 44.933 & 44.816 \\ Transformer\_Dec\_Imp + Syn + EZ (Both) & 37.556 & 37.183 & 37.622 & 37.283 & 36.102 & 37.149 \\ Transformer\_Enc\_Dec\_Imp + EZ (Both) & 45.966 & 46.005 & 44.840 & 44.722 & 45.698 & 45.446 \\ Transformer\_Enc\_Dec\_Imp + Syn + EZ (Both) & 45.569 & 46.081 & 45.597 & 45.264 & 45.658 & **45.634** \\ \hline \hline \end{tabular}
\end{table}
Table 6: BLEU evaluation of five-layer models with fused Primer-EZ on different random seeds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Conditions** & **Avg. BLEU** \\ \hline Transformer\_Base & N = 6, EPOCHS = 40 & **38.435** \\ Transformer\_Base & N = 5, EPOCHS = 35 & 36.521 \\ Transformer\_Base + EZ (Squared Leaky ReLU) & N = 5, EPOCHS = 35 & 37.289 \\ \hline Transformer\_Enc\_Imp & N = 6, EPOCHS = 40 & **46.302** \\ Transformer\_Enc\_Imp & N = 5, EPOCHS = 35 & 44.396 \\ Transformer\_Enc\_Imp + EZ (Squared Leaky ReLU) & N = 5, EPOCHS = 35 & 44.816 \\ \hline Transformer\_Dec\_Imp + Syn & N = 6, EPOCHS = 40 & 30.844 \\ Transformer\_Dec\_Imp + Syn & N = 5, EPOCHS = 35 & 33.801 \\ Transformer\_Dec\_Imp + Syn + EZ (Both) & N = 5, EPOCHS = 35 & **37.149** \\ \hline Transformer\_Enc\_Dec\_Imp & N = 6, EPOCHS = 40 & 41.602 \\ Transformer\_Enc\_Dec\_Imp & N = 5, EPOCHS = 35 & 39.389 \\ Transformer\_Enc\_Dec\_Imp + EZ (Both) & N = 5, EPOCHS = 35 & **45.446** \\ \hline Transformer\_Enc\_Dec\_Imp + Syn & N = 6, EPOCHS = 40 & 45.141 \\ Transformer\_Enc\_Dec\_Imp + Syn & N = 5, EPOCHS = 35 & 41.930 \\ Transformer\_Enc\_Dec\_Imp + Syn + EZ (Both) & N = 5, EPOCHS = 35 & **45.634** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The Traditional Mongolian-Chinese translation results.  
Figure 9: The structure of the Primer-EZ. (**a**) Multi-DConv-Head Attention (MDHA). (**b**) Squared ReLU.  
As we can see, the average BLEU values of Transformer_Base and Transformer_Enc_Imp were higher after adding Squared Leaky ReLU, indicating that Squared Leaky ReLU can speed up the convergence of the model and slightly improve the translation quality.  
Transformer_Dec_Imp + Syn + EZ, Transformer_Enc_Dec_Imp + EZ, and Transformer_Enc_Dec_Imp + Syn + EZ achieved the highest average BLEU scores, while the number of model parameters and training rounds were reduced, indicating that the improved model with a fused CNN layer can significantly improve translation quality. It can be seen that the addition of a CNN layer on the one hand made up for the insufficient number of neurons and insufficient fitting ability at the original decoder side, and on the other hand, \(Q\), \(K\), and \(V\), after the CNN layer, enhanced the extraction of local features, making it easier for the MMHPDA layer to judge the strength of association between words.