ChartCheck: Explainable Fact-Checking over Real-World Chart Images
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866917591099899904 |
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| author | Akhtar, Mubashara Subedi, Nikesh Gupta, Vivek Tahmasebi, Sahar Cocarascu, Oana Simperl, Elena |
| author_facet | Akhtar, Mubashara Subedi, Nikesh Gupta, Vivek Tahmasebi, Sahar Cocarascu, Oana Simperl, Elena |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_07453 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ChartCheck: Explainable Fact-Checking over Real-World Chart Images Akhtar, Mubashara Subedi, Nikesh Gupta, Vivek Tahmasebi, Sahar Cocarascu, Oana Simperl, Elena Computation and Language Computer Vision and Pattern Recognition 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 |
| title | ChartCheck: Explainable Fact-Checking over Real-World Chart Images |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.07453 |