Recent chart-authoring systems increasingly focus on natural-language input, enabling users to form a mental image of the chart they wish to create and express this intent using spoken instructions (spoken imagined-chart data). Yet these systems are predominantly trained on typed instructions written while viewing the target chart (typed existing-chart data). While the cognitive processes for describing an existing chart arguably differ from those for creating a new chart, the structural differences in the corresponding prompts remain underexplored. We present empirical findings on the structural differences among spoken imagined-chart instructions, typed imagined-chart instructions, and typed existing-chart instructions for chart creation, showing that imagined-chart prompts contain richer command formats, element specifications, and complex linguistic features, especially in spoken instructions. We then compare the performance of systems trained on spoken imagined-chart data versus typed existing-chart data, finding that the first system outperforms the second one on both voice and text input, highlighting the necessity of targeted training on spoken imagined-chart data. We conclude with design guidelines for chart-authoring systems to improve performance in real-world scenarios.

翻译：近年来，图表创作系统日益聚焦于自然语言输入，使用户能够对其希望创建的图表形成心理意象，并通过语音指令（语音想象图表数据）表达这一意图。然而，这些系统主要基于观看目标图表时撰写的文本指令（文本现有图表数据）进行训练。尽管描述现有图表的认知过程与创建新图表的过程可能存在差异，但相应提示的结构性区别仍未得到充分探索。我们呈现了关于语音想象图表指令、文本想象图表指令和文本现有图表指令在图表创建中结构性差异的实证研究结果，表明想象图表提示包含更丰富的命令格式、元素规范和复杂的语言特征，尤其在语音指令中更为显著。随后，我们比较了基于语音想象图表数据训练的系统与基于文本现有图表数据训练的系统的性能，发现前者在语音和文本输入上均优于后者，这凸显了对语音想象图表数据进行针对性训练的必要性。最后，我们提出了图表创作系统的设计准则，以提升其在真实场景中的表现。