## 5 Result Analysis
### Ablation Experiment  
To explore the specific effects of the two proposed methods, two ablation experiments are designed in this paper. The first is to retain the fusion method of cross-attention unchanged, and compare the effect of using CLIP pre-trained data on the translation results. The second is to use CLIP pre-trained data by comparing the effect of simple average fu  
Figure 11: Comparison of BLEU values of different sentence lengths on ZH–EN. Figure 10: Comparison of BLEU values of different sentence lengths on EN–ZH. sion and our cross-attention fusion method on the translation results. The principle of the average fusion method is to simply average the output of the transformer and the output of the GNN to predict the probability distribution of the target vocabulary. The model structure diagram of average fusion is similar to Figure 9, just replacing the cross-attention part with the average fusion function. The two comparison models in the first ablation experiment are AF + CLIP and CAF. The former uses both CAF and +CLIP, while the latter only uses CAF without +CLIP. The two comparison models in the second ablation experiment are CAF + CLIP and AF + CLIP. The former utilizes CAF and +CLIP, and the latter utilizes average fusion and +CLIP. The experimental results are shown in Table 2.  
By analyzing the experimental results shown in Table 2, the following can be obtained:  
1. The translation model CAF + CLIP that uses both cross-attention fusion and CLIP pre-training cannot effectively improve the performance of the model, but will cause a significant decline in the quality of the translation. This paper argues that the reason for this result is that the characteristics of CAF and +CLIP are not suitable for simultaneous use. The core of CAF is to fuse syntactic tree graph data and text data to improve the translation ability of the model. Its main advantage is that it can use the relationship between words in the syntactic tree graph data to improve the model's ability to understand the semantics of sentences. However, this approach may be replaced by CLIP pre-trained models that have learned rich semantic representations from multiple vision and language tasks using large-scale self-supervised learning. Using CAF and CLIP pre-trained models at the same time may lead to redundant information fusion, which affects the performance of the model. In addition, the CAF + CLIP model needs to process a large number of parameters during training, which may cause overfitting and increase the training time, thus affecting the performance of the model. Therefore, models pre-trained using cross-attention fusion or CLIP alone can better utilize their respective advantages and improve the performance of the model. 2. After using CLIP pre-training, the difference between the translation model using cross-attention fusion and the translation model using average fusion is not significant. However, in general, the translation model with cross-attention fusion performs better. The reason may be that cross-attention fusion can better utilize the information interaction between different models, thus improving the representation ability of the model. In the simple average model, the information of different models is simply averaged and fused, and the interactive information between different models cannot be fully utilized. In cross-attention fusion, each model can better understand the information of other models by paying attention to other models, thereby avoiding information conflicts between different models and improving the model performance. CAF can also avoid information conflicts between different models. In simple average models, information from different models may collide, resulting in poor model performance. To sum up, when performing text translation tasks, it is necessary to select an appropriate model structure and pre-training technology according to the characteristics and requirements of the task to improve the translation quality.