## 4 Experiment and Analysis
### Experimental Results  
Firstly, we assess the effectiveness of the CDML on the CMU-MOSI benchmark. Table 3: Perspective analysis results from multimodal data with different models on the CMU-MOSI dataset. By comparison, the CDML model demonstrates notable enhancements across all evaluation metrics when juxtaposed with the baselines. Regarding the 2 classification accuracy, Acc-2, in the sentiment task classification metric, CDML achieved 83.7%, showcasing an enhancement ranging from 1.3% to 5.7% in comparison to the baseline models. Regarding the classification metric F1, CDML improved by 1.5-7.7% compared with the baseline models. CDML reached 39.1% on the 7 classification accuracy Acc-7, an improvement of about 1.2-5.9% compared with the baseline models. Regarding the sentiment task regression metric Pearson's coefficient Corr, the model improved by about 0.021-0.049. All of the above metrics reached the optimal level in the baseline model.  
Subsequently, we assess the performance of the CDML model on the CMU-MOSEI benchmark. Table 4 showcases the empirical findings of diverse baselines, alongside CDML, within the CMU-MOSEI dataset. Evidently, the CDML model outperforms the alternative baselines notably. The primary reasons for this superiority are as follows: The proposed attention network can focus on filtering the modal information that is more important for the sentiment prediction task, and the generative adversarial constraint is added to the loss function, which helps the features of the translation modality to better approximate the source modality features during the translation process. In addition, the multitask learning strategy is used to collaboratively train the multimodal task and each single-modal auxiliary task, which helps the model pay more attention to the differences between different modalities, and then more comprehensively handle the sentiment prediction task and improve the prediction accuracy of the model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Model & Acc-7 (\(\uparrow\)) & Acc-2 (\(\uparrow\)) & F1 (\(\uparrow\)) & Corr (\(\uparrow\)) & MAE (\(\downarrow\)) \\ \hline RAVEN & 50.0 & 79.1 & 79.5 & 0.662 & 0.614 \\ MCTTN & 49.6 & 79.8 & 80.6 & 0.670 & 0.609 \\ MMIT & 51.1 & 80.1 & 80.3 & 0.628 & 0.610 \\ MISA & 51.2 & 80.5 & 80.8 & 0.662 & 0.629 \\ Self-MM & 51.0 & 80.8 & 81.2 & 0.674 & 0.609 \\ MoNIG & 50.0 & 81.0 & 81.5 & 0.600 & 0.688 \\ CMURT & 51.6 & 82.9 & 82.6 & 0.742 & 0.581 \\
**CDML** & **52.9** & **84.3** & **84.3** & **0.761** & **0.553** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with baselines on the CMU-MOSEI benchmark.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Model & Acc-7 (\(\uparrow\)) & Acc-2 (\(\uparrow\)) & F1 (\(\uparrow\)) & Corr (\(\uparrow\)) & MAE (\(\downarrow\)) \\ \hline RAVEN & 33.2 & 78.0 & 76.6 & 0.691 & 0.915 \\ MCTTN & 35.6 & 79.3 & 79.1 & 0.676 & 0.909 \\ MUT & 35.1 & 80.2 & 80.1 & 0.679 & 0.972 \\ MISA & 35.6 & 80.4 & 79.8 & 0.689 & 0.969 \\ Self-MM & 35.8 & 80.7 & 80.6 & 0.693 & 0.954 \\ MoNIG & 34.1 & 80.6 & 80.6 & 0.680 & 0.951 \\ CMJRT & 37.9 & 82.4 & 82.8 & 0.704 & 0.887 \\
**CDML** & **39.1** & **83.7** & **84.3** & **0.725** & **0.864** \\ \hline \hline \end{tabular} Bold is to show more clearly the optimal result under the same rubric. The arrows (\(\uparrow\) & \(\downarrow\)) and bold meanings of the other tables in the paper are consistent with this table.  
\end{table}
Table 3: Comparison with baselines on CMU-MOSI benchmark. \(\uparrow\) means higher is better and \(\downarrow\) means lower is better.