ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

Ahmed Masry, Do Long, 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
Venues:
ACL | 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
Bibkey:
Cite (ACL):
Ahmed Masry, Do Long, 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)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.177.pdf
Code
 vis-nlp/chartqa
Data
DVQAFigureQALEAF-QAPlotQA