Chart Question Answering: State of the Art and Future Directions

TL;DR

This survey reviews recent advances in Chart Question Answering (CQA), highlighting current methods, challenges, and future directions for systems that interpret visual data charts to answer natural language questions.

Contribution

It provides a comprehensive taxonomy, evaluates existing approaches, and discusses open challenges and future research opportunities in CQA.

Findings

CQA systems vary in input and output modalities.

Evaluation techniques for CQA are diverse and evolving.

Open challenges include improving accuracy and understanding complex questions.

Abstract

Information visualizations such as bar charts and line charts are very common for analyzing data and discovering critical insights. Often people analyze charts to answer questions that they have in mind. Answering such questions can be challenging as they often require a significant amount of perceptual and cognitive effort. Chart Question Answering (CQA) systems typically take a chart and a natural language question as input and automatically generate the answer to facilitate visual data analysis. Over the last few years, there has been a growing body of literature on the task of CQA. In this survey, we systematically review the current state-of-the-art research focusing on the problem of chart question answering. We provide a taxonomy by identifying several important dimensions of the problem domain including possible inputs and outputs of the task and discuss the advantages and limitations of proposed solutions. We then summarize various evaluation techniques used in the surveyed papers. Finally, we outline the open challenges and future research opportunities related to chart question answering.