EIGP: document-level event argument extraction with information enhancement generated based on prompts

Abstract

The event argument extraction (EAE) task primarily aims to identify event arguments and their specific roles within a given event. Existing generation-based event argument extraction models, including the recent ones focused on document-level event argument extraction, emphasize the construction of prompt templates and entity representations. However, they overlook the inadequate comprehension of model in document context structure information and the impact of arguments spanning a wide range on event argument extraction. Consequently, this results in reduced model detection accuracy. In this paper, we propose a prompt-based generation event argument extraction model with the ability of document structure information enhancement for document-level event argument extraction task based on prompt generation. Specifically, we use sentence abstract meaning representation (AMR) to represent the contextual structural information of the document, and then remove the redundant parts of the structural information through constraints to obtain the constraint graph with the document information. Finally, we use the encoder to convert the graph into the corresponding dense vector. We inject these vectors with contextual structural information into the prompt-based generation EAE model in a prefixed manner. When contextual information and prompt templates interact at the attention layer of the model, the generated structural information improves the generation by affecting attention. We conducted experiments on RAMS and WIKIEVENTS datasets, and the results show that our model achieves excellent results compared with the current advanced generative EAE model.