发表机构
Seoul National University（首尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对可视化语法与创作过程不匹配的问题，提出ggaction，将创作操作抽象为函数链，提升代码可解释性，并在人类和机器可解释性上优于Vega-Lite和ggplot2。
AI 中文摘要
图表可以是声明式的；但创建图表的过程并非如此。可视化语法通常将图表描述为完成的规范，而人们是通过一系列创作操作来构建图表的。这种不匹配使得可视化代码难以被人类理解，也难以让机器从人类意图中生成。ggaction通过建模图表创作过程本身来解决这一差距。在ggaction中，单个创作操作被抽象为函数，创作过程被表示为这些函数的链。这种表示更紧密地将图表设计者的创作意图与代码规范对齐，使得代码易于人类和机器（包括语言模型）理解。通过一系列评估，我们表明ggaction具有足够的表达能力来捕获常见的图表创作意图，并且在人类和机器的可解释性方面优于广泛使用的可视化语法，包括Vega-Lite和ggplot2。ggaction可在以下网址获取：此http URL。
英文摘要
A chart may be declarative; authoring it is not. Visualization grammars often describe charts as finished specifications, whereas people construct them through a sequence of authoring actions. This mismatch can make visualization code difficult for humans to interpret and for machines to generate from human intent. ggaction addresses this gap by modeling the chart authoring process itself. In ggaction, individual authoring actions are abstracted as functions, and the authoring process is expressed as a chain of these functions. This representation more closely aligns chart designers' authoring intent with code specifications, making the code easily understandable to both humans and machines, including language models. Through a series of evaluations, we show that ggaction is sufficiently expressive to capture common chart authoring intents and outperforms widely used visualization grammars, including Vega-Lite and ggplot2, in both human and machine interpretability. ggaction is available at github.com/ggaction/ggaction.
Comments21 pages
