## 5 Experiments
### Main Results  
We conducted five experiments for each incremental learning task using different random seeds, and reported the average performance of our method on the ACE2005-Chinese and DuEE datasets, comparing it with the aforementioned baseline methods. The results are shown in Tables 4 and 5. To visualize the trends more clearly, we also plotted the corresponding line chart, as shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**ACE2005-Chinese**} \\ \hline \multicolumn{10}{c}{**Methods**} & \multicolumn{5}{c}{**2-Way 5-Shot**} \\ \hline  & **1** & **2** & **3** & **4** & **5** & **1** & **2** & **3** & **4** & **5** \\ \hline Joint & / & / & / & / & 64.62 & / & / & / & / & 67.43 \\ Replay all & 93.22 & 68.87 & 58.21 & 62.92 & 61.73 & 93.22 & 72.64 & 65.17 & 66.25 & 64.26 \\ Fine-tune & 93.22 & 64.15 & 58.21 & 54.17 & 48.38 & 93.22 & 76.42 & 70.15 & 59.17 & 51.79 \\ \hline LwF & 93.22 & 65.09 & 59.20 & 65.42 & 61.73 & 93.22 & 80.19 & 71.64 & 70.83 & 61.01 \\ KCN & 93.22 & 65.09 & 58.71 & 67.08 & 58.12 & 93.22 & 80.19 & 71.14 & 72.50 & 64.26 \\ KT & 93.22 & 65.09 & 64.18 & 65.42 & 59.57 & 93.22 & 80.19 & 68.66 & 75.00 & 68.59 \\ EMP & 90.08 & 68.12 & 62.34 & 68.27 & 64.62 & 90.08 & 82.01 & 72.75 & 73.94 & 68.08 \\ ICE-PL\&O & 93.22 & 64.38 & 59.41 & 62.76 & 57.83 & 93.22 & 66.23 & 61.54 & 63.9 & 60.56 \\ \hline ICD(Ours) & **93.22** & **80.19** & **73.13** & **74.28** & **71.48** & **93.22** & **83.96** & **78.12** & **75.97** & **73.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Recall@3 performance of every sub-task on ACE2005-Chinese for comparative experiment._ \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**DuEE**} \\ \hline \multicolumn{10}{c}{**Methods**} & \multicolumn{5}{c}{**2-Way 5-Shot**} \\ \hline  & **1** & **2** & **3** & **4** & **5** & **1** & **2** & **3** & **4** & **5** \\ \hline Joint & / & / & / & / & 84.08 & / & / & / & / & 87.99 \\ Replay all & 98.55 & 82.61 & 89.39 & 89.58 & 81.68 & 98.55 & 84.78 & 83.84 & 88.80 & 84.38 \\ Fine-tune & 98.55 & 86.96 & 86.87 & 83.40 & 75.38 & 98.55 & 91.30 & 86.87 & 81.08 & 76.28 \\ \hline LwF & 98.55 & 87.68 & 89.90 & 90.73 & 86.49 & 98.55 & 88.04 & 89.39 & 91.51 & 87.09 \\ KCN & 98.55 & 86.96 & 87.88 & 90.73 & 84.38 & 98.55 & 89.13 & 88.38 & 92.66 & 86.67 \\ KT & 98.55 & 86.23 & 88.89 & 90.73 & 84.38 & 98.55 & 87.68 & 88.89 & 91.12 & 86.49 \\ EMP & 97.10 & 87.68 & 89.13 & 91.22 & 86.49 & 97.10 & 89.13 & 89.39 & 92.00 & 89.49 \\ ICE-PL\&O & 98.55 & 84.22 & 86.34 & 85.19 & 79.28 & 98.55 & 84.26 & 86.66 & 85.90 & 79.37 \\ \hline ICD(Ours) & **98.55** & **92.03** & **92.93** & **93.82** & **90.39** & **98.55** & **94.93** & **93.43** & **94.60** & **92.49** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Recall@3 performance of every sub-task on DuEE for comparative experiment._From the results, we can observe the following:  
* Compared to previous incremental-learning baseline methods, our approach significantly outperforms these methods across all sub-tasks. Specifically, in the ACE 5-shot and 10-shot, and DuEE 5-shot and 10-shot settings, our method improves the final recall rate by 6.86%, 4.53%, 7.69%, and 4.53%, respectively, compared to the best baseline model. This demonstrates the clear effectiveness of our method in handling continuously arriving data. * Although the Joint method retrains using all available data, it is not always the optimal baseline method. In our setup, the initial task has a sufficient number of samples, while the training samples for subsequent new tasks are limited. This imbalance in sample sizes affects the performance of the Joint method. In contrast, our approach improves upon Joint by 6.86%, 5.69%, 3.90%, and 3.00% in the ACE 5-shot and 10-shot, and DuEE 5-shot and 10-shot settings, respectively. This indicates that our method effectively mitigates the negative impact of sample imbalance in few-shot incremental learning conditions.