# Research on an Event Extraction Framework Based on Two-Step Prompt Learning for Chinese Policy
## 2 Related Work  
Event extraction, a challenging task in Natural Language Processing (NLP), focuses on identifying event arguments that serve as key roles in an event and assigning them to their corresponding pre-defined event types. In the Chinese policy domain, the type of declared policy represents the type of event, and the conditions for applying the corresponding type of policy represent the thesis element of the event. This paper focuses on the events of \" (financial support)\" and \" (recognition of title)\" in the context of Chinese policies. There are two types of event extraction methods: pipeline methods and joint models. The pipeline approach divides the event extraction task into multiple subtasks and processes them in a sequential manner. It has the disadvantage of error propagation between different subtasks. In order to solve such problems, joint models have been proposed for event extraction, which usually contain joint inference and joint modeling. Joint inference uses integrated learning to optimize a model through an overall objective function. The joint model treats the event structure as a dependency tree and then transforms the extraction task into a dependency tree prediction. It can recognize trigger words and extract arguments simultaneously. The inference and modeling share hidden layer parameters, which can avoid performance degradation due to error propagation. From a technical point of view, event extraction methods can also be categorized into template and rule-based methods and deep learning-based methods. Early event extraction methods were based on template matching or regular expressions. The researchers used syntactic analysis and semantic constraints to identify events in sentences [7]. In 1995, [PERSON] et al. [8] used semantic frames and phrase patterns to represent the extraction patterns of events. Over-incorporating semantic information from wordnets can approach human results in specific domains. However, the performance of template methods is highly dependent on the language and is poorly portable. In recent years, most event extraction parties are based on deep learning. Deep learning methods are more generalized than traditional template- and rule-based methods. Early deep learning methods used various neural networks to extract events, such as convolutional neural network (CNN)-based models ([PERSON] et al., 2015 [9]), recurrent neural network (RNN)-based models ([PERSON] et al., 2016 [10]; [PERSON] et al., 2018 [11]), and graph neural network (GNN)-based models ([PERSON] et al., 2018 [12]; [PERSON] et al., 2021 [13]). Pre-trained models have been shown to be powerful in language comprehension and generation ([PERSON] et al., 2019 [14]; [PERSON] et al., 2019 [15]). Approaches based on pre-trained language models (PLMs-based) can be divided into two categories: fine-tuning and prompt-tuning. Fine-tuning approaches aim to design multiple neural networks to migrate pre-trained language models to event extraction tasks. Based on the way the event extraction task is modeled, the existing fine-tuning work can be further classified into three groups: **(1) Classification-based approaches:** In 2021, [PERSON] et al. (2021) proposed a neural architecture with a novel bi-directional entity-level recursive decoder that extracts the ontology by combining the ontology roles of contextual entities. In 2020, [PERSON] et al. (2020) proposed a trigger-relative sequence with several types of representation of a trigger-aware sequence encoder. **(2) Methods based on machine reading comprehension:** Naturally inducing linguistic knowledge from pre-trained language models. [PERSON] et al. consider implicit interactions between roles by adding mutual constraints to the templates, while in 2021, [PERSON] et al. (2021) utilized data enhancement to improve performance. However, such methods can only predict role-by-role, which is inefficient and usually leads to sub-optimal performance. **(3) Generation-based approaches:** In 2021, [PERSON] et al.