Chart Question Answering: State of the Art and Future Directions
- URL: http://arxiv.org/abs/2205.03966v1
- Date: Sun, 8 May 2022 22:54:28 GMT
- Title: Chart Question Answering: State of the Art and Future Directions
- Authors: E. Hoque, P. Kavehzadeh, A. Masry
- Abstract summary: Chart Question Answering (CQA) systems typically take a chart and a natural language question as input and automatically generate the answer.
We systematically review the current state-of-the-art research focusing on the problem of chart question answering.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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