## 5 Result Analysis  
### Comparison of Fusion Methods  
In this paper, we use the transformer [27] as the baseline model, and the model + CLIP pre-trained by the transformer and GNN is implemented first. The experimental results are shown in Table 1. Compared to the baseline model for the EN-ZH task, the translation model using CLIP pre-training shows an increase in the BLEU value of 3.37 percentage points, while the translation model using CAF shows an increase of 1.31 percentage points. Similarly, compared to the baseline model for the ZH-EN task, the translation model using CLIP pre-training shows an increase in the BLEU value of 3.18 percentage points, while the translation model using cross-attention fusion shows an increase of 1.03 percentage points. By comparing these three translation models, we can observe that the model pre-trained with CLIP achieves the best performance. Whether in the EN-ZH or ZH-EN translation tasks, the CLIP-pretrained translation model consistently outperforms the model with CAF by more than two percentage points in terms of the BLEU score. We hypothesize that the information obtained after CLIP pre-training can more fully reflect the syntactic and semantic information of the sentence. Analyzing the experimental results as shown in Table 1, we can draw the following conclusions:  
1. The +CLIP model outperforms the CAF model. We explore the reasons behind the superior performance of the +CLIP model by comparing its differences with the CAF model in the machine translation task: 1. Different data processing approaches: The CAF method utilizes a GNN to process syntactic tree graph data and employs a transformer model to handle textual data. Finally, a cross-attention mechanism is used to fuse them together. But, the +CLIP method leverages the CLIP model for multimodal pretraining, mapping both textual and visual data into the same multimodal feature space. This ability to fuse multimodal features enables the CLIP model to better understand and model the relationship between syntactic tree graphs and text, capturing the semantic information of sentences more effectively and thus achieving a better performance in machine translation tasks. 2. Different feature fusion approaches: CAF utilizes a cross-attention mechanism to fuse the syntactic tree graph and textual data. However, +CLIP achieves feature fusion using multimodal pretraining. This fusion approach maps the vector representations of text and images into the same multimodal feature space, allowing better utilization of this feature space containing information from both modalities during fine-tuning. The use of this shared feature space may contribute to improved generalization and representation capabilities, thereby enhancing the machine translation performance. 3. Semantic understanding capability: The +CLIP approach achieves cross-modal semantic understanding of text and images using multimodal pretraining, capturing the semantic correlations between them. However, the CAF method focuses on a semantic understanding of the sentence structure and dependencies. It utilizes a GNN to process syntactic tree graph data and employs a transformer model to handle textual data, aiming to better understand the structural composition and dependency relationships within sentences. While both methods possess semantic understanding capabilities, their emphasis and implementation differ, leading to variations in their performance. These differences in feature fusion, semantic understanding and implementation contribute to the superior performance of the CLIP model: 2. By comparing the translation results of the two EN-ZH and ZH-EN language pairs, it can be seen that the effect of syntactic information fusion is more obvious in EN-ZH. The main reason is that English is more complex in syntactic structure than Chinese.