# Research on an Event Extraction Framework Based on Two-Step Prompt Learning for Chinese Policy
## 4 Experiment Results
### Main Results  
Table 5 shows the experimental results of the proposed TPEE and baseline model. It can be seen that among the baseline models, the prompt-based model performs better than all the others overall. One of the classification-based models, the PAIE model, performed the best in the argument recognition task with an FI-score of 71.78%. The TPEE achieves a higher FI-score of 73.92% in the argument recognition task, realizing an absolute improvement of 2.14%. In addition, the TPEE performs equally well in the argument categorization task, obtaining a 72.81% F1-score, which is 2.26% higher than the 70.55% of the PAIE model. This shows that the prompt design method in this paper is more consistent with the characteristics of the policy text and improves the overall extraction effect. In addition, on the test set, by comparing the extraction results of TPEE with those of PAIE, it can be found that TPEE is superior to PAIE in extracting long and continuous parameters. Details are described in the case study.  
Table 6 presents the informative argument extraction results for various models on the Duee-fin test set. It can be seen that the PAIE model performs strongly in the Arg Identification task, obtaining an F1-score of 78.09%, while the TPEE further improves on this task, reaching an F1-score of 78.93%, achieving an absolute improvement of 0.84%. In the Arg Classification task, the TPEE also performs well, achieving an F1-score of 77.25%, which outperforms the 76.51% of the PAIE model, an improvement of 0.74%. Compared to other baseline models, such as BertMRC and BART-Gen, the TPEE demonstrates more significant advantages in both the argument recognition and classification tasks, especially in the classification task, where it outperforms the Rbt3 model with 62.71% and the BertMRC model with 71.86%. These results suggest that event extraction in the financial domain is also more suitable for the use of a prompt-based approach, and that our prompt design approach has not only made enhancements in the policy domain, but also advances in the financial domain, with strong migratory properties.  
On the policy and financial datasets, the prompt-based approach is significantly superior to other approaches in terms of F1 values. This is mainly due to the fact that policy data and financial data are usually highly structured and rules-based texts, often containing clear terms, conditions, regulations, etc., with a high degree of logic and consistency  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Arg Identification (\%)**} & \multicolumn{3}{c}{**Arg Classification (\%)**} \\ \cline{2-6}  & P & R & F & P & R & F \\ \hline Rbt3 & - & - & - & 43.32 & 46.96 & 44.95 \\ BART-Gen & 73.86 & 42.94 & 53.63 & 66.34 & 37.81 & 48.16 \\ BertMRC & - & - & - & 75.36 & 58.26 & 65.71 \\ PAIE & 75.12 & 68.73 & 71.78 & 73.59 & 67.74 & 70.55 \\ GPLinker & - & - & - & 52.75 & 62.30 & 57.01 \\ \hline Ours & 77.80 & 70.41 & 73.92 & 76.53 & 69.43 & 72.81 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Informative argument extraction results on DEE-Policy test set.  
of content. The prompt-based approach guides the model through carefully designed instructions or prompts to extract information from these explicit rules, greatly reducing ambiguity and error. Compared with traditional model training methods, the prompt-based approach can make better use of domain knowledge.