## 4 Experiment
### Ablation Study and Analysis  
In order to verify the influence of three key modules in the model, Marker-Based Mutual Learning (MBML)-enhanced training, dual template generation module, and Marker Classification-Enhanced Decoding (MCED) module, we conducted ablation experiments. The results are presented in Table 4.  
Firstly, we ablated the MBML module. Compared with the DMPL method, the F1 scores for both datasets decreased by more than 3%. Additionally, in Section 4.4, we explored the hyperparameter \(\beta\). From Figure 4, we observe that, compared to the model without the mutual learning enhancement module (\(\beta=0\)), our model shows performance improvements across all values of \(\beta\). This indicates that mutual learning enhancement training enables each individual generation module to exchange information, leading to a deeper understanding of the task. For the MCED module, we first conduct ablation analysis on the Restaurant-ACOS and Laptop-ACOS datasets. The results indicate that the performance degrades when the MCED module is removed, compared to the DMPL, which is decreased by 1.52% and 0.47%, respectively. This indicates that the MCED module is able to enhance term feature semantic learning by interacting with aspect terms and opinion terms through BIO tags. Furthermore, to investigate the MCED module's ability to capturing implicit sentiments, we \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Restaurant-ACOS**} & \multicolumn{4}{c}{**Laptop-ACOS**} \\  & **EAEO** & **EAIO** & **IAEO** & **IAIO** & **Contain-I** & **EAEO** & **EAIO** & **IAEO** & **IAIO** & **Contain-I** \\ \hline Extract-Classify & 48.34 & 27.52 & 37.96 & 36.96 & 30.92 & 33.81 & 15.24 & 37.42 & 17.00 & 29.07 \\ Paraphrase & 64.18 & 40.61 & 51.02 & 52.43 & 47.55 & 45.09 & 35.51 & 52.74 & 35.15 & 42.76 \\ GEN\_SCL\_NAT & 63.06 & 38.37 & 45.48 & 55.45 & 47.44 & 43.74 & 31.44 & 52.94 & 33.54 & 40.28 \\ MVP & 63.86 & 42.54 & 51.05 & 52.74 & 47.62 & 45.43 & 37.06 & 57.00 & 34.18 & 41.95 \\ ILO & 63.29 & 35.34 & 51.34 & 56.46 & 47.41 & 46.55 & 36.09 & 54.63 & 38.27 & 42.37 \\ DLO & 63.48 & 38.89 & 50.00 & 56.71 & 45.87 & 45.58 & 37.44 & 55.64 & 37.63 & 42.41 \\ \hline DMPL & **66.45** & **47.88** & **51.79** & **58.73** & **50.37** & **47.50** & **37.44** & **58.12** & **39.44** & **46.18** \\ \hline \hline \end{tabular}  
* The best results of previous baselines are bolded.  
\end{table} 
Table 3: Implicit sentiment analysis. performed an ablation analysis on subsets focused on implicit sentiment analysis. As shown in Table 5, removing the MCED module resulted in average performance declines of 2.79% and 3.02% on the EAIO and IAIO subsets, respectively. MCED uses \"NULL\" to express explicit terms, which helps the model to distinguish between the semantics of explicit and implicit terms. Additionally, it uses BIO tags containing location information to interact with feature semantics in the decoding stage, which can significantly enhance the model's prediction of implicit terms. For the dual-template prompt generation module, we ablate the \(\mathcal{T}_{ACSO}\) and \(\mathcal{T}_{OSA}\) prompt templates, respectively. Removing either prompt template also eliminates the MBML module. As observed in Table 4, the F1 scores with only the \(\mathcal{T}_{ACSO}\) or only \(\mathcal{T}_{OSA}\) templates are significantly lower than the model without the mutual learning training module. The single-template generation model, such as Paraphrase, makes predictions of the elements of a quadruple based on templates, which to some extent restricts the output positions of the elements, as the decoding process can only proceed linearly. In contrast, the dual-template model is built upon the disorder of the four elements, swapping the positions of aspect terms and opinion terms and setting up two predicted outputs for modeling. The multiple paths of output provide the model with various decoding sequences.