ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
Ahmed Masry, Xuan Long Do, Jia Qing Tan, Shafiq Joty, Enamul Hoque
Abstract
Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus on such complex reasoning questions as their questions are template-based and answers come from a fixed-vocabulary. In this work, we present a large-scale benchmark covering 9.6K human-written questions as well as 23.1K questions generated from human-written chart summaries. To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark, the evaluation also reveals several challenges in answering complex reasoning questions.- Anthology ID:
- 2022.findings-acl.177
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2022
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2263–2279
- Language:
- URL:
- https://aclanthology.org/2022.findings-acl.177
- DOI:
- 10.18653/v1/2022.findings-acl.177
- Cite (ACL):
- Ahmed Masry, Xuan Long Do, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. 2022. ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2263–2279, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning (Masry et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.findings-acl.177.pdf
- Code
- vis-nlp/chartqa
- Data
- ChartQA, DVQA, FigureQA, LEAF-QA, PlotQA