## The Proposed Model
### The Lazy Layer Question  
In [15], a variety of different encoder and decoder layer combinations were studied to test the impact on the translation. The experimental results revealed that the encoder is more effective and important than the decoder. Increasing or decreasing the number of layers of the decoder of the Transformer does not significantly affect the model performance. Based on this previous work, we determined the required number of layers of encoders and decoders in the X-Transformer. We experimented with various encoder and decoder combinations for comparison and used the original Transformer model as a baseline for benchmark, as shown in Table 1.  
From Table 1, we discover that, in the case of the same number of encoder layers, reducing the number of decoder layers can improve the translation performance of the model and reduce the training time. We believe the reason for the improvement in performance is that the encoder of the Transformer is a very important part to understand the input corpus. After the encoder has a considerable understanding of the original corpus, then, the target language sentence is generated by the decoder. Since the decoder is used for generation, the self-attention mechanism calculation is employed by mixing the original input and the partially generated results to predict the next output step. The presence of a large number of encoder layers seems to force the model to refocus on some tokens, which are not important or had already been attended before. Therefore, the translation accuracy cannot be improved.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Encoder Layers** & **Decoder Layers** & **BLEU** & **Training Time (under the Same Env)** \\ \hline
6 & 6 & 29.33 & 1\(\times\) \\
6 & 4 & 29.26 & 0.82\(\times\) \\
6 & 2 & 30.99 & 0.68\(\times\) \\
8 & 6 & 29.26 & 1.19\(\times\) \\
8 & 4 & 29.82 & 1.01\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the number of layers of encoder and decoder in the Transformer model.  
Humanoid learning translation works as, for example, asking students who have a considerable knowledge of a language to translate sentences to the target language through what they learned. If students are asked to attempt to understand and translate what has been written in the target language, the improvement may be obvious in the beginning. However, students have to give too much attention when they are repeatedly asked to focus on understanding and translating the target language. In these, the learning outcomes are not met. On the contrary, they mostly stop paying attention and slow down the learning pace. This phenomenon is similar to the phenomenon called \"learning plateau\" in educational psychology [22]. As shown in Figure 3, as the learning time increases, there is a period of low efficiency when a plateau is reached. At this time, the manner to continue the learning process must be changed. Returning to the translation model, a decoder with fewer layers is recommended. However, if the decoder is reduced to one layer, the generated results will be unstable. Finally, we decided to use a double-layer decoder for the X-Transformer.