论文标题
图表问题回答：艺术和未来的方向
Chart Question Answering: State of the Art and Future Directions
论文作者
论文摘要
诸如条形图和线路图之类的信息可视化对于分析数据和发现关键见解非常普遍。人们经常分析图表以回答他们想到的问题。回答此类问题可能具有挑战性，因为它们通常需要大量的感知和认知努力。图表问题回答（CQA）系统通常将图表和自然语言问题作为输入，并自动生成答案以促进视觉数据分析。在过去的几年中，关于CQA任务的文献越来越多。在这项调查中，我们系统地回顾了当前的最新研究，重点是图表问题的问题。我们通过确定问题域的几个重要维度（包括任务的可能输入和输出）来提供分类法，并讨论提议的解决方案的优势和局限性。然后，我们总结了被调查论文中使用的各种评估技术。最后，我们概述了与图表问题回答有关的开放挑战和未来的研究机会。
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.
