ChartCheck: Explainable Fact-Checking over Real-World Chart Images

Mubashara Akhtar, Nikesh Subedi, Vivek Gupta, Sahar Tahmasebi, Oana Cocarascu +1 more
11/13/2023
cs.CLcs.CV

No official code found. Best implementation: mubasharaak/ChartCheck (10 stars, last commit 2024-02-16).

Abstract

Whilst fact verification has attracted substantial interest in the natural language processing community, verifying misinforming statements against data visualizations such as charts has so far been overlooked. Charts are commonly used in the real-world to summarize and communicate key information, but they can also be easily misused to spread misinformation and promote certain agendas. In this paper, we introduce ChartCheck, a novel, large-scale dataset for explainable fact-checking against real-world charts, consisting of 1.7k charts and 10.5k human-written claims and explanations. We systematically evaluate ChartCheck using vision-language and chart-to-table models, and propose a baseline to the community. Finally, we study chart reasoning types and visual attributes that pose a challenge to these models

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100Python, Jupyter NotebookApr 26, 20232 years ago

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Cite this paper

@article{akhtar2023chartcheck,
  title  = {ChartCheck: Explainable Fact-Checking over Real-World Chart Images},
  author = {Mubashara Akhtar and Nikesh Subedi and Vivek Gupta and Sahar Tahmasebi and Oana Cocarascu and Elena Simperl},
  year   = {2023},
  eprint = {2311.07453},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.07453v2}
}

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