  Extracting informative arguments of events from news articles is a
challenging problem in information extraction, which requires a global
contextual understanding of each document. While recent work on document-level
extraction has gone beyond single-sentence and increased the cross-sentence
inference capability of end-to-end models, they are still restricted by certain
input sequence length constraints and usually ignore the global context between
events. To tackle this issue, we introduce a new global neural generation-based
framework for document-level event argument extraction by constructing a
document memory store to record the contextual event information and leveraging
it to implicitly and explicitly help with decoding of arguments for later
events. Empirical results show that our framework outperforms prior methods
substantially and it is more robust to adversarially annotated examples with
our constrained decoding design. (Our code and resources are available at
https://github.com/xinyadu/memory_docie for research purpose.)
