| Challenge: | a dataset for chart question answering is constructed from visualization notebooks . data visualizations are an essential modality for communicating complex information about data. |
| Approach: | They propose a dataset for chart question answering constructed from visualization notebooks . they use real-world, multi-view charts paired with natural language questions . |
| Outcome: | The proposed dataset is constructed from student-authored visualization notebooks . it features real-world, multi-view charts paired with natural language questions . initial evaluations highlight significant performance gaps . |
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| Challenge: | Chart question answering (CQA) is a multimodal task for evaluating the reasoning capabilities of vision-language models. |
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| Challenge: | Existing datasets that focus on complex reasoning questions do not address such questions as they are template-based and answers come from a fixed-vocabulary. |
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| Challenge: | Chart question answering (CQA) is a crucial area of Visual Language Understanding. |
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| Challenge: | Chart question answering (ChartQA) tasks are a critical part of visualization charts. |
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Santiago Castro, Mahmoud Azab, Jonathan Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, Rada Mihalcea
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NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset (2021.findings-emnlp)
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| Challenge: | Existing question answering datasets lack numerical reasoning and reasoning processes . current research on numerical reasoning focuses on simple calculations . |
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