## 4 Experiment
### Result Analysis  
Table 2 shows the experimental results of our model compared with the baseline models, achieving 65.73% and 47.06%, respectively. According to the observation, the F1 score of our method is better than the baseline model. MVP, ILO, Paraphrase, and other generative models based on t5 model are significantly better than the joint extraction method based on the Bert pre-training model on the ACOS task. MVP, DLO, and ILO all adopt multiple prompt templates. We set the parameters according to the best results in the original paper [8] using five templates for the MVP model and seven templates for DLO and ILO. The more templates are designed, the more paths are generated, and the more time it takes to train. Compared with the generative model with multiple templates, we achieve, on average, 5.3% and 3.4% higher F1 scores on the Restaurant-ACOS and Laptop-ACOS datasets with only two templates. The precision is as high as 71.07% and 52.26%, which is 11.54% and 8.43% higher than other models on average. Compared with F1 and Pre, the Rec value is lower. This is due to the fact that the DMLP model obtains the features of different elements with the help of markers in the template to enhance the interactive iteration of the two template generation processes in the decoding stage. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Restaurant-ACOS**} & \multicolumn{3}{c}{**Laptop-ACOS**} & \multicolumn{3}{c}{**Rest15**} \\
**Method** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** \\ \hline Extract–classify & 42.95 & 54.08 & 47.87 & 39.21 & 30.35 & 34.22 & 32.93 & 33.41 & 35.44 \\ Paraphrase & 59.51 & 59.11 & 59.31 & 43.87 & 42.81 & 43.33 & 49.53 & 48.06 & 48.85 \\ GEN\_SCL\_NAT & 58.88 & 59.27 & 59.07 & 42.65 & 42.39 & 42.52 & 47.22 & 47.07 & 47.50 \\ MVP & 59.58 & 58.73 & 59.15 & 42.80 & 42.65 & 42.72 & 50.68 & 49.09 & 49.87 \\ ILO & 58.55 & 60.38 & 59.45 & 43.71 & 43.49 & 43.60 & 47.23 & 47.87 & 47.68 \\ DLO & 60.47 & 60.40 & 60.43 & 44.93 & 44.62 & 44.77 & 48.92 & 48.69 & 48.86 \\ \hline DMPL & 71.07 & 61.14 & **65.73** & 52.26 & 44.81 & **47.06** & 55.34 & 51.71 & **52.88** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results on DPML. The best results of previous baselines are bolded. Figure 4: Effects of \(\alpha\) and \(\beta\) on the validation set. Figure (**a**) shows the changes in F1 scores for the Restaurant-ACOS and Laptop-ACOS datasets as the value of \(\alpha\) varies. Panel (**b**) illustrates the changes in F1 scores for the Restaurant-ACOS and Laptop-ACOS datasets as the value of \(\beta\) varies. In addition, we compare the performance of the model on the Rest15 dataset in the ASQP task. The F1 score of DMPL outperforms the existing T5-based model by 4.33% on average, which also shows that DMPL is suitable for all aspect sentiment quadruple prediction tasks. According to the different types of aspects and opinions, five subsets are constructed by splitting Restaurant-ACOS and Laptop-ACOS. Table 3 presents the F1 scores of our model and the baseline model on the subsets. In this way, the ability of the model to learn implicit words is explored. From Table 3, we can see that the model has the highest quadruple prediction power for the subset of the EAEO type. The EAEO subset only contains explicit aspect terms and opinion terms, while other subsets containing implicit terms pose more challenges to the model. Our method achieves the highest F1 score in the vast majority of subsets. Among them, the datasets containing IA or IO can predict the quadruples more effectively, especially in the Contain-I dataset, which mixes explicit and implicit aspect terms and opinion terms, which shows obvious advantages. The F1 score for the Laptop-ACOS dataset improved by 3.42%, and the F1 score for the Restaurant-ACOS dataset increased by 2.75%.