  We present a novel document-level model for finding argument spans that fill
an event's roles, connecting related ideas in sentence-level semantic role
labeling and coreference resolution. Because existing datasets for
cross-sentence linking are small, development of our neural model is supported
through the creation of a new resource, Roles Across Multiple Sentences (RAMS),
which contains 9,124 annotated events across 139 types. We demonstrate strong
performance of our model on RAMS and other event-related datasets.
