Multi-Sentence Argument Linking

Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, Benjamin Van Durme


Abstract
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.
Anthology ID:
2020.acl-main.718
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8057–8077
Language:
URL:
https://aclanthology.org/2020.acl-main.718
DOI:
10.18653/v1/2020.acl-main.718
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.718.pdf
Video:
 http://slideslive.com/38928805
Data
FrameNet