# Neural Machine Translation of Electrical Engineering Based on Vector Fusion
## 5 Discussion  
### Vector Fusion Experiment  
From the analysis of the experimental results shown in Table 1, it can be observed that:  
* The improved structure of the residual connection proposed in this paper and the four fusion experiments have improved the translation effect of the model, which verifies that the stacked structure of the network structure will indeed cause the underlying information to be lost in the process of model training, and the information contained in the hidden layer vector output by the multi-layer encoder unit is biased and different in terms of syntax and lexical meaning.
* As shown in Table 1, the BLEU (bilingual evaluation understudy) value of the translation model after the residual connection is increased by 0.23 percentage points, while the BLEU value of the average fusion translation model is increased by 0.15 percentage points, which is slightly lower than the effect of the residual connection. The reason is that although the average fusion method adds the output vector of the middle layer encoder unit, it also reduces the weight of the top encoder unit and the output vector of the word embedding layer, so their final BLEU value is slightly lower than the residual connection.
* It can be seen from the experimental results that the splicing fusion and the gate mechanism fusion have similar improvements in the translation effect of the model, but the BLEU value of the gate mechanism fusion is slightly higher because the calculation process of the two fusion methods is similar.
* Comparing the BLEU values of the four fusion methods, the average fusion is the lowest because the average fusion assigns the same fixed weight to each vector that needs to be fused. However, in fact, the information of each layer of encoder units contained in the fusion vector finally output by the encoder is definitely different. The fusion of the splicing and the gate mechanism is centered because both use the fully connected layer to convert the dimensions of the seven vectors to be fused inside and compress them into one vector, which is better than the average fusion effect. Weight fusion can achieve the best model translation performance improvement because this method performs random weight assignment directly outside the multiple vectors that need to be fused and makes it change with the subsequent model training process, which is simple and effective.
* It can be seen from the comparative data in Table 2 that although the N-1 system fusion method has also achieved some improvements in the translation effect, making the model learn more source language information from multiple angles for the model stack structure of Transformer, only the vector fusion of the output vectors at the top level of multiple encoders cannot make good use of the source language information at the bottom of the encoder. However, the vector fusion method proposed in this paper fuses the multiple hidden layer vectors and the underlying word embedding in the encoder, makes good use of the underlying word meaning information inside the encoder, solves the problem of underlying information loss caused by model stacking to a certain extent, obtains a more comprehensive source language feature vector at the encoder end, and achieves better translation effect, which fully proves the effectiveness of the vector fusion method proposed in this paper.