{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/chartqa-a-benchmark-for-question-answering","title":"ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning","arxiv_id":"2203.10244","date":"2022-03-19","proceeding":"Findings (ACL) 2022 5","authors":["Ahmed Masry","Do Xuan 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.","url_abs":"https://arxiv.org/abs/2203.10244v1","url_pdf":"https://arxiv.org/pdf/2203.10244v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"chartqa-a-benchmark-for-question-answering","repo_url":"https://github.com/vis-nlp/chartqa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"chart-question-answering","task_name":"Chart Question Answering"},{"task_slug":"logical-reasoning","task_name":"Logical Reasoning"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[{"slug":"chartqa","name":"ChartQA","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/chart-question-answering-on-chartqa","task":"Chart Question Answering","dataset":"ChartQA","model":"VisionTapas-OCR","rank_in_archive_order":25,"of":27,"metrics":{"1:1 Accuracy":"45.5"},"uses_additional_data":false},{"leaderboard":"/sota/chart-question-answering-on-plotqa","task":"Chart Question Answering","dataset":"PlotQA","model":"VL-T5-OCR","rank_in_archive_order":4,"of":6,"metrics":{"1:1 Accuracy":"66.0"},"uses_additional_data":false},{"leaderboard":"/sota/chart-question-answering-on-plotqa","task":"Chart Question Answering","dataset":"PlotQA","model":"VisionTapas-OCR","rank_in_archive_order":6,"of":6,"metrics":{"1:1 Accuracy":"53.9"},"uses_additional_data":false},{"leaderboard":"/sota/chart-question-answering-on-realcqa","task":"Chart Question Answering","dataset":"RealCQA","model":"crct - baseline","rank_in_archive_order":1,"of":5,"metrics":{"1:1 Accuracy":"0.178733575026565"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.10244","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}