An AMR-based Link Prediction Approach for Document-level Event Argument
  Extraction
Yuqing Yang Qipeng Guo Xiangkun Hu Yue Zhang Xipeng Qiu Zheng Zhang
Recent works have introduced Abstract Meaning Representation (AMR) for
Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a
useful interpretation of complex semantic structures and helps to capture
long-distance dependency. However, in these works AMR is used only implicitly,
for instance, as additional features or training signals. Motivated by the fact
that all event structures can be inferred from AMR, this work reformulates EAE
as a link prediction problem on AMR graphs. Since AMR is a generic structure
and does not perfectly suit EAE, we propose a novel graph structure, Tailored
AMR Graph (TAG), which compresses less informative subgraphs and edge types,
integrates span information, and highlights surrounding events in the same
document. With TAG, we further propose a novel method using graph neural
networks as a link prediction model to find event arguments. Our extensive
experiments on WikiEvents and RAMS show that this simpler approach outperforms
the state-of-the-art models by 3.63pt and 2.33pt F1, respectively, and do so
with reduced 56% inference time. The code is availabel at
https://github.com/ayyyq/TARA.