# Research on an Event Extraction Framework Based on Two-Step Prompt Learning for Chinese Policy
## 4 Experiment Results
### Ablation Performance
#### 4.4.2 Influence of Prompt in Different Position  
The TPEE method proposed in this paper includes two-prompt learning. The first is to use the prompt to interact with the input text in the BART decoder so that the slots in the prompt used to extract characters contain text information. The second is in the dynamic weighted pooling phase, where information for each role is fused with each token in the prompt, allowing the model to focus on information for the other roles, which is conducive  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Arg Identification (\%)**} & \multicolumn{4}{c}{**Arg Classification (\%)**} \\ \cline{2-7}  & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline Rbt3 & - & - & - & 55.40 & 72.25 & 62.71 \\ BertMRC & - & - & - & 71.31 & 72.43 & 71.86 \\ BART-Gen & 75.24 & 44.62 & 53.63 & 68.50 & 40.30 & 50.75 \\ PAIE & 79.92 & 76.33 & 78.09 & 78.43 & 74.68 & 76.51 \\ GPLinker & - & - & - & 50.35 * & 65.19 * & 56.82 * \\ \hline Ours & 81.28 & 76.71 & 78.93 & 79.44 & 75.18 & 77.25 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Informative argument extraction results on DuEE-Fin test set. * indicates values taken from the original paper.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Arg Classification (\%)**} \\ \cline{2-3}  & **DEE-Policy** & **DuEE-Fin** \\ \hline Ours & 72.81 \(\pm\) 0.78 & 77.25 \(\pm\) 0.45 \\ \hline Dynamic Weighted Pooling & 71.26 \(\pm\) 1.04 & 76.92 \(\pm\) 0.98 \\ Meaning & 70.78 \(\pm\) 0.64 & 76.55 \(\pm\) 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study on two datasets.  
to extracting it during the extraction phase. In order to explore the influence of these two prompts on parameter extraction, this paper designed a set of ablation experiments, and the results are shown in Table 8.  
In order to verify the effect of the two-step prompt, four sets of experiments were designed. Prompt_all_detailed is the fine design for both prompts. Prompt_one_detailed is the fine design for the first prompt. Prompt_two_detailed is the fine design for the second prompt, and Prompt_all means no fine design for both prompts. From the experimental results, it is can be seen that when fine-grained design is applied to both prompts, the F1 value of the argument classification on the two datasets (DEE-Policy, Duee-fin) reaches the highest value, which is 72.81% and 77.25%, respectively, and rises by 0.79%, and 0.63% compared to the F1 value of both prompts with no fine-grained design. In contrast, when only the second location of the prompt was fine-tuned, it yielded F1 values of 72.53% and 76.95% on the two datasets, which were only 0.28% and 0.30% lower than prompt_all_detailed. In contrast, the F1 values for the fine design of only the first prompt decreased by 0.55% and 0.53%. It shows that the design of both prompts has an effect on the overall extraction effect of the model, but the effect of the second prompt design on the extraction effect is more obvious compared to the first one.