FAMuS: Frames Across Multiple Sources

Siddharth Vashishtha, Alexander Martin, William Gantt, Benjamin Van Durme, Aaron Steven White
11/9/2023
cs.CL

No official code found. Best implementation: FACTSlab/FAMuS (5 stars, CC-BY-SA-4.0, last commit 2024-07-05).

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 \emph{across documents} can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that \emph{report} on some event, paired with underlying, genre-diverse (non-Wikipedia) \emph{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: \emph{source validation} -- determining whether a document is a valid source for a target report event -- and \emph{cross-document argument extraction} -- full-document argument extraction for a target event from both its report and the correct source article. We release both FAMuS and our models to support further research.

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Code Implementations(1)

FACTSlab/FAMuSCommunity100%
CC-BY-SA-4.0

FAMuS -- Frames Across Multiple Sources.

51Shell, PythonOct 12, 20232 years agoCC-BY-SA-4.0

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Cite this paper

@article{vashishtha2023famus,
  title  = {FAMuS: Frames Across Multiple Sources},
  author = {Siddharth Vashishtha and Alexander Martin and William Gantt and Benjamin Van Durme and Aaron Steven White},
  year   = {2023},
  eprint = {2311.05601},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.05601v1}
}

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