## 4 Experiment
### Ablation Study
#### 4.3.2 Fusion Strategy with Relevant Coefficients  
Before performing multimodal fusion in the model, we adjusted the unimodal representations based on the relevant coefficients computed between unimodal representations and their respective initial modality representations. Subsequently, these representations were concatenated with the global multimodal representation. To validate the effectiveness of our proposed fusion strategy, we conducted experiments on both Chinese and English datasets. We compared the performance of models with and without considering unimodal relevant coefficients, where the unimodal representations, computed after self-attention and subsequent Bi-LSTMs, were directly concatenated with the global multimodal representation, and then fed into the fusion and prediction module. We also compared these results with the standard version of CCDA. The comparative experimental results are shown in Tables 9 and 10.  
According to Tables 9 and 10, it is evident that in multimodal fusion, the model's performance significantly improves when unimodal features are augmented with relevant coefficients compared to direct concatenation. Specifically, there is a 1.5% improvement in Acc-7. Therefore, the use of relevant coefficients in the multimodal feature fusion stage enables the model to analyze the relations between the self-attention modality representations and the source feature representations, and, thus, to achieve higher accuracy on multiclassification.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **MAE (\(\downarrow\))** & **Corr (\(\uparrow\))** & **Acc-7 (\(\uparrow\))** & **Acc-5 (\(\uparrow\))** & **Acc-2 (\(\uparrow\))** & **F1 (\(\uparrow\))** \\ \hline Only self-attention & 0.734 & 0.764 & 45.1 & 51.9 & 83.0 & 83.0 \\ Only cross-attention & 0.722 & 0.787 & 46.4 & 53.2 & 84.4 & 84.5 \\ Standard CCDA & **0.696** & **0.807** & **48.0** & **54.8** & **55.7** & **85.6** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Impact of dual-attention in CCDA on CMU-MOSI. Where \(\uparrow\) indicates that the higher the metric the stronger the performance of the model, and \(\downarrow\) is the opposite. Bold numbers indicate the model with the best results at that metric.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **MAE (\(\downarrow\))** & **Corr (\(\uparrow\))** & **Acc-5 (\(\uparrow\))** & **Acc-3 (\(\uparrow\))** & **Acc-2 (\(\uparrow\))** & **F1 (\(\uparrow\))** \\ \hline Only self-attention & 0.443 & 0.602 & 40.7 & 65.7 & 78.4 & 78.3 \\ Only cross-attention & 0.415 & 0.613 & 41.9 & 66.8 & 79.9 & 79.9 \\ Standard CCDA & **0.393** & **0.628** & **43.3** & **68.3** & **81.1** & **81.0** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Impact of dual-attention in CCDA on CH-SIMS. Where \(\uparrow\) indicates that the higher the metric the stronger the performance of the model, and \(\downarrow\) is the opposite. Bold numbers indicate the model with the best results at that metric.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **MAE (\(\downarrow\))** & **Corr (\(\uparrow\))** & **Acc-7 (\(\uparrow\))** & **Acc-5 (\(\uparrow\))** & **Acc-2 (\(\uparrow\))** & **F1 (\(\uparrow\))** \\ \hline Direct Concat & 0.713 & 0.790 & 46.5 & 53.8 & 85.2 & 85.2 \\ Standard CCDA & **0.696** & **0.807** & **48.0** & **54.8** & **85.7** & **85.6** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Impact of correlation coefficients in fusion strategy on CMU-MOSI. Where \(\uparrow\) indicates that the higher the metric the stronger the performance of the model, and \(\downarrow\) is the opposite. Bold numbers indicate the model with the best results at that metric.