Type-aware Decoding via Explicitly Aggregating Event Information for
  Document-level Event Extraction
Gang Zhao Yidong Shi Shudong Lu Xinjie Yang Guanting Dong Jian Xu Xiaocheng Gong Si Li
Document-level event extraction (DEE) faces two main challenges:
arguments-scattering and multi-event. Although previous methods attempt to
address these challenges, they overlook the interference of event-unrelated
sentences during event detection and neglect the mutual interference of
different event roles during argument extraction. Therefore, this paper
proposes a novel Schema-based Explicitly Aggregating~(SEA) model to address
these limitations. SEA aggregates event information into event type and role
representations, enabling the decoding of event records based on specific
type-aware representations. By detecting each event based on its event type
representation, SEA mitigates the interference caused by event-unrelated
information. Furthermore, SEA extracts arguments for each role based on its
role-aware representations, reducing mutual interference between different
roles. Experimental results on the ChFinAnn and DuEE-fin datasets show that SEA
outperforms the SOTA methods.