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Recent works have introduced Abstract Meaning Representation (AMR) forDocument-level Event Argument Extraction (Doc-level EAE), since AMR provides auseful interpretation of complex semantic structures and helps to capturelong-distance dependency. However, in these works AMR is used only implicitly,for instance, as additional features or training signals. Motivated by the factthat all event structures can be inferred from AMR, this work reformulates EAEas a link prediction problem on AMR graphs. Since AMR is a generic structureand does not perfectly suit EAE, we propose a novel graph structure, TailoredAMR Graph (TAG), which compresses less informative subgraphs and edge types,integrates span information, and highlights surrounding events in the samedocument. With TAG, we further propose a novel method using graph neuralnetworks as a link prediction model to find event arguments. Our extensiveexperiments on WikiEvents and RAMS show that this simpler approach outperformsthe state-of-the-art models by 3.63pt and 2.33pt F1, respectively, and do sowith reduced 56% inference time. The code is availabel athttps://github.com/ayyyq/TARA.

An AMR-based Link Prediction Approach for Document-level Event Argument Extraction