## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.5.2 Ablation Tests  
To investigate the impact of different module modifications on model detection performance and verify the effectiveness of the proposed method, the impact of different fusion methods on model performance was studied by conducting comparative experiments using the random seed of \(S_{3}\). The calculated BLEU values are summarized in Tables 3-5:  
By comparing the BLEU values of different models in Table 6, it can also be concluded that our MCNMT model can improve the translation quality, and its BLEU value is 2.58 higher than that of the baseline model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Fusion Method** & **BLEU Value** \\ \hline Transformer & None & 39.50 \\ \hline \multirow{4}{*}{Encoder} & Dynamic fusion mechanism & 40.89 \\  & Knowledge extraction paradigm & 40.26 \\  & Dynamic fusion mechanism, Knowledge extraction paradigm & 40.53 \\ \hline \multirow{4}{*}{Decoder} & Dynamic fusion mechanism & 39.16 \\  & Knowledge extraction paradigm & 40.62 \\  & Dynamic fusion mechanism, Knowledge extraction paradigm & 39.77 \\ \hline \multirow{2}{*}{Transformer\_Temp\_Kno} & Encoder: Dynamic fusion mechanism & \multirow{2}{*}{42.08} \\  & Encoder: Knowledge extraction paradigm & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation tests. Different fusion strategies applied on the encoder end are shown in the Transformer_Encoder section in Table 6. Although knowledge extraction can improve the translation quality of the model, it is not as effective as the dynamic fusion mechanism, and the use of both methods together does not improve the performance of the algorithm, indicating that the dynamic fusion method on the encoder end covers the effectiveness of knowledge extraction. In addition, different fusion strategies used on the decoder end are presented in the Transformer_Decoder section in Table 6. Under the same settings, the BLEU value is 0.34 lower than that of the baseline model, indicating that the dynamic fusion mechanism is ineffective at the encoder end. The comparative experimental results show that it is correct to use different context extraction methods on the encoder and decoder ends separately because the responsibilities of the encoder and decoder are different. Since the encoder obtains contextual information by modeling input sentences, the use of external contextual information can improve its modeling performance. Therefore, even though sentence-level knowledge extraction can be applied on the encoder end, the effect is not as good as that of the dynamic fusion mechanism. The main responsibility of the decoder is to generate the target sentence through the input source representation, which involves the transformation problem of semantic spaces. Therefore, using a knowledge extraction paradigm that can learn the output distribution is more suitable for the decoder, which helps the model generate better sentences. Figures 13 and 14 show the loss function and accuracy curves of the models with six different fusion methods on the training and validation sets, respectively. (Encoder_Dy represents using the dynamic fusion mechanism on the encoder end; Encoder_Dis represents using the knowledge extraction paradigm on the encoder end; Encoder_Dy_Dis represents using both the dynamic fusion mechanism and the knowledge extraction paradigm on the encoder end; Decoder_Dy represents using the dynamic fusion mechanism on the decoder end; Decoder_Dis represents using the knowledge extraction paradigm on the decoder end; Decoder_Dy_Dis represents using both the dynamic fusion mechanism and the knowledge extraction paradigm on the decoder end. Each model represent an improvement based on the Transformer baseline model). From Figures 15 and 16, for both the training and validation sets, the loss function and accuracy of Decoder_Dis curves and Encoder_Dy curves are optimal and similar, while Decoder_Dy curves show the worst performance.