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.

本篇综述论文旨在系统回顾当前关于数据可视化分析中图表问题回答（Chart Question Answering，CQA）系统的最新研究进展。论文采用分类法，从任务输入输出和解决方案等多个角度进行了整理和讨论，并总结了相关试验评估技术，同时还指出了与 CQA 相关的挑战和未来研究机遇。

图表问答：现状和未来方向

Chart Question Answering: State of the Art and Future Directions

Chart question answering (CQA) is a task used for assessing chart
comprehension, which is fundamentally different from understanding natural
images. CQA requires analyzing the relationships between the textual and the
visual components of a chart, in order to answer general questions or infer
numerical values. Most existing CQA datasets and models are based on
simplifying assumptions that often enable surpassing human performance. In this
work, we address this outcome and propose a new model that jointly learns
classification and regression. Our language-vision setup uses co-attention
transformers to capture the complex real-world interactions between the
question and the textual elements. We validate our design with extensive
experiments on the realistic PlotQA dataset, outperforming previous approaches
by a large margin, while showing competitive performance on FigureQA. Our model
is particularly well suited for realistic questions with out-of-vocabulary
answers that require regression.

本文针对图表理解任务的挑战，提出了一种新型的图表问答模型。我们提出的联合分类与回归模型使用了共现注意力变换器，在处理文本语言和视觉元素的复杂互动时表现出色。在经过广泛的实验验证后，我们的模型在真实场景下的 PlotQA 数据集上表现出卓越的性能，也在 FigureQA 数据集上表现出竞争性的结果。此外，我们的模型特别适用于需要回归的非词汇库答案的实际问题。

图表理解的分类回归

Classification-Regression for Chart Comprehension

Chart question answering (CQA) is a newly proposed visual question answering
(VQA) task where an algorithm must answer questions about data visualizations,
e.g. bar charts, pie charts, and line graphs. CQA requires capabilities that
natural-image VQA algorithms lack: fine-grained measurements, optical character
recognition, and handling out-of-vocabulary words in both questions and
answers. Without modifications, state-of-the-art VQA algorithms perform poorly
on this task. Here, we propose a novel CQA algorithm called parallel recurrent
fusion of image and language (PReFIL). PReFIL first learns bimodal embeddings
by fusing question and image features and then intelligently aggregates these
learned embeddings to answer the given question. Despite its simplicity, PReFIL
greatly surpasses state-of-the art systems and human baselines on both the
FigureQA and DVQA datasets. Additionally, we demonstrate that PReFIL can be
used to reconstruct tables by asking a series of questions about a chart.

该研究提出了一种名为 PReFIL 的算法，通过融合图像和语言特征来智能地回答关于数据可视化的图表的问题，达到了优于现有系统和人类基线的效果，并且还能够通过一系列问题对图表进行重构。