FAMuS: Frames Across Multiple Sources

Siddharth Vashishtha, Alexander Martin, William Gantt, Benjamin Van Durme, Aaron White


Abstract
Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event across documents can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that report on some event, paired with underlying, genre-diverse (non-Wikipedia) source articles for the same event. Events and (cross-sentence) arguments in both report and source are annotated against FrameNet, providing broad coverage of different event types. We present results on two key event understanding tasks enabled by FAMuS: source validation—determining whether a document is a valid source for a target report event—and cross-document argument extraction—full-document argument extraction for a target event from both its report and the correct source article.
Anthology ID:
2024.naacl-long.457
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8243–8266
Language:
URL:
https://aclanthology.org/2024.naacl-long.457
DOI:
Bibkey:
Cite (ACL):
Siddharth Vashishtha, Alexander Martin, William Gantt, Benjamin Van Durme, and Aaron White. 2024. FAMuS: Frames Across Multiple Sources. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 8243–8266, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
FAMuS: Frames Across Multiple Sources (Vashishtha et al., NAACL 2024)
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PDF:
https://aclanthology.org/2024.naacl-long.457.pdf
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 2024.naacl-long.457.copyright.pdf