Affiliation:
1. PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
Abstract
Document-level event argument extraction (DEAE) aims to identify the arguments corresponding to the roles of a given event type in a document. However, arguments scattering and arguments and roles overlapping make DEAE face great challenges. In this paper, we propose a novel DEAE model called Role Knowledge Prompting for Document-Level Event Argument Extraction (RKDE), which enhances the interaction between templates and roles through a role knowledge guidance mechanism to precisely prompt pretrained language models (PLMs) for argument extraction. Specifically, it not only facilitates PLMs to understand deep semantics but also generates all the arguments simultaneously. The experimental results show that our model achieved decent performance on two public DEAE datasets, with 3.2% and 1.4% F1 improvement on Arg-C, and to some extent, it addressed the overlapping arguments and roles.
Funder
National Social Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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