  Event extraction has long been treated as a sentence-level task in the IE
community. We argue that this setting does not match human information-seeking
behavior and leads to incomplete and uninformative extraction results. We
propose a document-level neural event argument extraction model by formulating
the task as conditional generation following event templates. We also compile a
new document-level event extraction benchmark dataset WikiEvents which includes
complete event and coreference annotation. On the task of argument extraction,
we achieve an absolute gain of 7.6% F1 and 5.7% F1 over the next best model on
the RAMS and WikiEvents datasets respectively. On the more challenging task of
informative argument extraction, which requires implicit coreference reasoning,
we achieve a 9.3% F1 gain over the best baseline. To demonstrate the
portability of our model, we also create the first end-to-end zero-shot event
extraction framework and achieve 97% of fully supervised model's trigger
extraction performance and 82% of the argument extraction performance given
only access to 10 out of the 33 types on ACE.
