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.
@article{arxiv.1911.03766,
title = {Multi-Sentence Argument Linking},
author = {Seth Ebner and Patrick Xia and Ryan Culkin and Kyle Rawlins and Benjamin Van Durme},
journal= {arXiv preprint arXiv:1911.03766},
year = {2020}
}