Dual-level AMR Injection for Prompt-based Event Argument Extraction

Abstract

Document-level event argument extraction (EAE) aims to recognize arguments involved in an event based on trigger throughout the document. According to event types, previous prompt-based researches focus on designing templates and promote the interaction between argument roles and the input through Pre-trained Language Models (PLMs). However, these methods hardly explore semantic structures of input documents, such as the trigger-argument information. In this paper, we inject Abstract Meaning Representation (AMR) graph from sentence and document perspectives into the prompt-based EAE model to leverage semantic information and prior knowledge simultaneously. We design the event inspired prompt template and construct the AMR graphs for the different-grained input, which contain relations between arguments and the trigger and help to bring semantic structure into the prompt-based EAE model. What’s more, we introduce a dual-level encoder for AMR graphs construction. It consists of a document-level encoder to capture cross-sentence arguments and a sentence-level encoder to reduce the probability of extracting incorrect role arguments. Comprehensive experimental results on two benchmarks show the effectiveness of our proposed approach.