# Role Knowledge Prompting for Document-Level Event Argument Extraction
## 2 Related Work  
**Document-level event argument extraction**. DEAE is an extremely challenging subtask of event extraction. DEAE can be traced back to the role-filling task from MUC conferences [17], which requires retrieval of the scenario-specific participating entities and attribute values. The TAC Knowledge Base Population slot filling challenge ([[https://tac.nist.gov/2017/KBP/index.html](https://tac.nist.gov/2017/KBP/index.html)]([https://tac.nist.gov/2017/KBP/index.html](https://tac.nist.gov/2017/KBP/index.html)), accessed on 13 November 2017) is similar to this task. Traditional event extraction methods [18; 19; 20; 21; 22] rely on hand-crafted features and patterns. With the ongoing development of neural networks, further extracted event arguments were based on the distributional features [8; 9; 23; 24; 25]. Recently, zero-shot learning [26], multi-modal integration [27], and weakly supervised [28] methods have been adopted to improve event extraction. EAE methods can be grouped into three categories. The first is the approach that perceives the context globally from the documents and classifies it by obtaining semantic representations at different granularities (word granularity, sentence granularity, and document granularity) [23; 29; 30; 31]. [PERSON] et al. used the local features of the arguments to assist in role classification [18]. [PERSON] et al. proposed a dynamic multi-pooling convolutional neural network model [8]. With the development of deep learning, event extraction is a sequence labeling task [31; 32; 33; 34], but these methods pale in comparison to an arbitrary location and deep semantic relations and cannot solve argument overlapping well. The second is to capture the unique features of a document using graph convolutional networks (GCNs) by modeling graph models such as syntactic semantic graphs, event connectivity graphs, and heterogeneous graphs to capture the interaction of arguments. The DBRNN enhanced the representation by bridging associated words on syntactic dependency trees [24]. [PERSON] et al. constructed heterogeneous graphs of sentences and entity mentions to strengthen the interaction between sentences and entity mentions [35]. The third is to transform event extraction into tasks such as reading comprehension and multi-round question-and-answer tasks [36; 37], formulating the roles as questions described in natural language and extracting the arguments by answering these questions in context. This makes better use of the a priori information of the roles' categories, but such methods require setting multiple relevant questions, and the model can only answer one question at a time. As the generative pretrained models become more powerful, such as T5 [38] and BART [39], they show extraordinary text generation capabilities, and recently, several extraction jobs have been converted to be completed as generative tasks [40]. [PERSON] et al. tackled DEAE with an end-to-end neural network model for conditional text generation [16]. Text2 Event put forth a sequence-to-structure generation paradigm that could extract events directly from text in an end-to-end manner [15]. In contrast to these approaches, our approach emphasizes the interaction between the arguments and can generate all the arguments simultaneously. **Prompt tuning**. Different from the \"pretraining + fine-tuning\" paradigm, prompt learning involves adapting downstream tasks to PLMs, which can be regarded as a retrieval method for knowledge already stored in PLMs and has been widely used for text classification [41], relation extraction [42], event extraction [43; 44], and entity classification [45]. Researchers have made efforts to determine how to design prompt templates. Automatic searching of discrete prompts [46], gradient-guided searches [47], and continuous prompts including P-tuning [48] and prefix-tuning [49] were successively proposed. Recently, some studies have tried to integrate external knowledge into the prompt design.