论文标题
NL4DV：一种用于从自然语言查询中生成数据可视化的分析规范的工具包
NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries
论文作者
论文摘要
自然语言界面（NLIS）在视觉数据分析方面表现出了巨大的希望，使人们可以灵活地指定并与可视化相互作用。但是，开发可视化NLI仍然是一项具有挑战性的任务，需要低级自然语言处理（NLP）技术以及视觉分析任务和可视化设计的知识。我们提出NL4DV，这是一种用于自然语言驱动数据可视化的工具包。 NL4DV是一个Python软件包，它作为输入表格数据集和有关该数据集的自然语言查询。作为响应，该工具包返回一个分析规范，该规范模型为JSON对象，其中包含数据属性，分析任务以及与输入查询相关的VEGA-LITE规格列表。这样一来，NL4DV的可视化开发人员可能没有NLP背景，使他们能够创建新的可视化NLI或在其现有系统中纳入自然语言输入。我们通过四个示例来证明NL4DV的用法和功能：1）在Jupyter笔记本中使用天然语言渲染可视化，2）开发NLI来指定和编辑Vega-Lite图表，3）重新创建DataTone系统中的数据歧义性小部件，以及4）将语音输入输入以创建一个多型型可视化系统。
Natural language interfaces (NLIs) have shown great promise for visual data analysis, allowing people to flexibly specify and interact with visualizations. However, developing visualization NLIs remains a challenging task, requiring low-level implementation of natural language processing (NLP) techniques as well as knowledge of visual analytic tasks and visualization design. We present NL4DV, a toolkit for natural language-driven data visualization. NL4DV is a Python package that takes as input a tabular dataset and a natural language query about that dataset. In response, the toolkit returns an analytic specification modeled as a JSON object containing data attributes, analytic tasks, and a list of Vega-Lite specifications relevant to the input query. In doing so, NL4DV aids visualization developers who may not have a background in NLP, enabling them to create new visualization NLIs or incorporate natural language input within their existing systems. We demonstrate NL4DV's usage and capabilities through four examples: 1) rendering visualizations using natural language in a Jupyter notebook, 2) developing a NLI to specify and edit Vega-Lite charts, 3) recreating data ambiguity widgets from the DataTone system, and 4) incorporating speech input to create a multimodal visualization system.
