# Generating Chinese Event Extraction Method Based on ChatGPT and Prompt Learning
## 3 Model
### Annotation Data Generation Based on ChatGPT  
To address the issue of scarce annotated data in event extraction, this paper utilizes the content generation capability of ChatGPT to generate an event extraction annotated corpus. This process involves two modules: recall and generation, as shown in Figure 2.  
1. Recall module: A continuous dialogue is established to allow ChatGPT to recall the event extraction knowledge it acquired during training. During this phase, questions related to event extraction and requests for data generation are posed to ChatGPT while continuously guiding and correcting its answers. Through extensive training on a large amount of textual data, ChatGPT learns the inherent logic of language and improves its generative ability by predicting the next word or sentence, accumulating a significant amount of semantic knowledge. In the first phase, an \"activation question library\" related to event extraction is created after multiple attempts. By continuously asking ChatGPT questions from the \"activation question library\", the model is guided to recall the knowledge it acquired during training regarding event extraction. The \"activation question library\" includes the basic definition of event extraction tasks, particularly the meaning of trigger words and event arguments, the construction of commonly used datasets for event extraction, and relevant examples. Correct answers are encouraged and supported, while incorrect answers are corrected and guided,  
Figure 1: Generative event extraction model graph based on ChatGPT and prompt learning.  
thus activating [PERSON]'s relevant knowledge on event extraction and establishing sufficient contextual connections.
2. Generation module: To enable ChatGPT to generate sentences that meet the task requirements in various contexts based on given trigger words and event arguments, the process begins with the random extraction of event trigger words. A trigger word is randomly selected from a schema event library as the trigger word for the current task. Then, a question-and-answer filling mechanism based on rules is used to ask [PERSON] about the corresponding event arguments for the newly initiated conversation based on the trigger word. The randomly extracted trigger word and its related event arguments are combined to form the task requirements for the current iteration. The task requirements and different contextual prompts are then inputted into ChatGPT. Through leveraging prior knowledge and guided by the dialogue, [PERSON] is directed to generate sentences in various contexts that fulfill the task requirements. Finally, the generated corpus is manually reviewed and edited to ensure quality and accuracy. Human intervention allows for better control over the content and logic of the generated corpus while eliminating potential errors or inappropriate text.  
Before training the pre-trained language model on task-specific data, it is beneficial to pre-train the model in the relevant domain through supervised training [32]. This effectively improves the model's performance in that domain. Therefore, in the case of the generative model BART, which has been trained on a large-scale general corpus, task pre-finetuning is performed. The event extraction annotated corpus generated by ChatGPT is used to fine-tune the model's parameters in the information extraction domain. This allows the model to acquire domain-specific knowledge in event extraction, correcting the deviation between general tasks and event extraction tasks.