## 5 Experiment
### Experimental Results and Discussion
#### 5.3.2 Experimental Results of the Five-Layer Models  
Experiments were conducted under the conditions of N = 5 and epochs = 35 to verify whether the above interpretations were correct. The parameter counts for the five-layer models are shown in Table 4, and the experimental results for each model are shown in Table 5.  
The contents in parentheses in Table 5 indicate the changes relative to Table 3. The poorer experimental results for the five-layer models were to be expected, as the total number of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Number of Parameters** \\ \hline Transformer\_Base & 45,894,085 \\ Transformer\_Enc\_Imp & 46,942,645 \\ Transformer\_Dec\_Imp + Syn & 46,157,253 \\ Transformer\_Enc\_Dec\_Imp & 47,205,813 \\ Transformer\_Enc\_Dec\_Imp + Syn & 47,205,813 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The number of parameters for each five-layer model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **100** & **6421** & **2169** & **500** & **256** & **Avg. BLEU** \\ \hline Transformer\_Base & 38.377 & 38.280 & 38.566 & 38.362 & 38.588 & 38.435 \\ Transformer\_Enc\_Imp & 46.158 & 45.661 & 46.994 & 46.161 & 46.535 & **46.302** \\ Transformer\_Dec\_Imp + Syn & 37.183 & 28.481 & 36.939 & 35.264 & 16.351 & 30.844 \\ Transformer\_Enc\_Dec\_Imp & 41.337 & 44.774 & 35.002 & 45.556 & 41.341 & 41.602 \\ Transformer\_Enc\_Dec\_Imp + Syn & 46.195 & 44.892 & 45.273 & 46.170 & 43.176 & 45.141 \\ \hline \hline \end{tabular}
\end{table}
Table 3: BLEU evaluation of six-layer models on different random seeds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **100** & **6421** & **2169** & **500** & **256** & **Avg. BLEU** \\ \hline Transformer\_Base & 36.575 & 36.686 & 36.533 & 36.711 & 36.099 & 36.521 (\(\downarrow\)1.914) \\ Transformer\_Enc\_Imp & 44.049 & 43.758 & 44.765 & 44.797 & 44.610 & **44.396** (\(\downarrow\)1.906) \\ Transformer\_Dec\_Imp + Syn & 36.244 & 36.374 & 25.571 & 35.433 & 35.382 & 33.801 (\(\uparrow\)2.957) \\ Transformer\_Enc\_Dec\_Imp & 41.068 & 42.649 & 36.973 & 43.258 & 32.996 & 39.389 (\(\downarrow\)2.213) \\ Transformer\_Enc\_Dec\_Imp + Syn & 45.758 & 44.197 & 37.822 & 37.973 & 43.901 & 41.930 (\(\downarrow\)3.211) \\ \hline \hline \end{tabular}
\end{table}
Table 5: BLEU evaluation of five-layer models on different random seeds.  
model parameters and training rounds were reduced. The six-layer Transformer_Dec_Imp + Syn achieved an abnormally low score, with the network parameters corresponding to the random seed 256, pulling down the average BLEU value and leading to a better experiment result for the five-layer Transformer_Dec_Imp + Syn, which should be an anomalous event.  
The same conclusion can be drawn from these two experiments, in descending order of the average BLEU value: Transformer_Enc_Imp, Transformer_Enc_Dec_Imp + Syn, Transformer_Enc_Dec_Imp, Transformer_Base, and Transformer_Dec_Imp + Syn.  
The loss curves and accuracy curves of the five models also corroborated the above conclusions. As can be seen in Figure 7a-d and Figure 8a,b, the five models had the same order of merit in these four curves, corresponding to the order of the average BLEU scores achieved by the model. However, in Figure 8c,d, the order of the Transformer_Enc_Dec_Imp + Syn and Transformer_Enc_Dec_Imp curves was reversed, but in terms of the average BLEU score, Transformer_Enc_Dec_Imp + Syn won slightly by 0.849, and the more plausible explanation is that Transformer_Enc_Dec_Imp + Syn had better generalization performance.  
More importantly, the experiments with the five-layer model confirmed the conjectures made in Section 5.3.1, which pointed the way to the next improvements.  
Figure 7: Loss curves and accuracy curves of six-layer models on training set and validation set. (**a**) Training set loss curve. (**b**) Validation set loss curve. (**c**) Training set accuracy curve. (**d**) Validation set accuracy curve.