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
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- 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
- Cite (ACL):
- Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, and Benjamin Van Durme. 2020. Multi-Sentence Argument Linking. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8057–8077, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Sentence Argument Linking (Ebner et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.acl-main.718.pdf
- Data
- FrameNet