# Role Knowledge Prompting for Document-Level Event Argument Extraction
## 1 Introduction  
Event extraction is an extremely challenging information extraction task [1; 2; 3] that extracts events and corresponding arguments from a text. It provides effective structured information for knowledge graph construction [4], recommendation systems [5; 6], etc. Event extraction is usually divided into two subtasks: event detection, which identifies an event's trigger words, and event argument extraction (EAE), which identifies the event arguments corresponding to a given trigger word and their roles. As in Figure 0(a), given the trigger word _\"annihilate\"_ with the event type _\"conflict.attack.n/a.\"_, the EAE model can extract roles such as _\"attacker\"_, _\"target\"_, and _\"place\"_ from sentence S1, and the arguments are _\"Russian air forces\"_, _\"ISIS oil tankers\"_, and _\"Aleppo province\"_. In Figure 0(b), the trigger word is _\"airst strike\"_, and the arguments _\"target\"_, _\"attacker\"_, and _\"instrument\"_ are scattered in sentences S2, S3, and S4 (called argument scattering). In particular, the same argument may play different roles in the event (called argument overlapping). For example, _\"Yement\"_ is both the target and place. Meanwhile, there are also cases where the same role corresponds to multiple arguments (called role overlapping). For example, there are other places aside from _\"Yement\"_. Arguments scattering and arguments and roles overlapping make document-level EAE (DEAE) more challenging than sentence-level EAE (SEAE) [7]. The main EAE approach is to learn the pairwise information between the trigger words and arguments. Some scholars use this as a classification task to directly locate and extract trigger words and arguments in the text and then classify them. Many of these works solve this problem by using sequence labeling techniques based on neural networks [8; 9; 10; 11]. However, such methods cannot effectively solve the problem of arguments and roles overlapping. Other studies converted this problem into machine reading comprehension or question-and-answer tasks [12; 13; 14], where a set question-and-answer template allows the model to find text spans of trigger words and arguments. However, such methods split the argument extraction into multiple tasks, where only one argument can be extracted at a time, and eventually, multiple rounds of the question-and-answertask are required to be completed, with the results of the previous round being passed to the next round, a process that lacks interaction between arguments. The generative pretraining approach enables the model to generate all arguments simultaneously through the learning capability of the pretrained models (PLMs) [15; 16] while incorporating prompt learning to stimulate the potential of the PLMs better. However, the lack of interaction between the prompt template and the roles leads to the templates not providing accurate prompting well. Inspired by the fact that humans need prior domain knowledge to understand text, it is observed that role knowledge plays a pivotal role in understanding contextual semantics. As in the example in Figure 1b, in sentence S, _\"Scud-type Borkan-1 (Volcano-1) missile\"_ is a certain type of missile, and if the meaning of the role _\"instrument\"_ is known to be a tool, which refers to the tool used in an air strike event, then it is predicted simply that _\"Scud-type Borkan-1 (Volcano-1) missile\"_ is an argument for the instrument in an air strike event. Similarly, as a city, _\"Yemen\"_ is very likely to perform the role of the target or place. Therefore, it can be considered that role knowledge is the bridge between textual semantics and arguments. Can we utilize role knowledge to guide prompting for sufficiently precise elicitation of PLMs and simultaneously extract all the arguments in events? On this basis, we propose a novel approach called role knowledge prompting for document-level event argument extraction (RKDE), which leverages role knowledge to guide templates for the precise prompting of PLMs.