Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary input modality to direct manipulation for visual analytics can provide an engaging user experience. It enables users to focus on their tasks rather than having to worry about how to operate visualization tools on the interface. In the past two decades, leveraging advanced natural language processing technologies, numerous V-NLI systems have been developed in academic research and commercial software, especially in recent years. In this article, we conduct a comprehensive review of the existing V-NLIs. In order to classify each paper, we develop categorical dimensions based on a classic information visualization pipeline with the extension of a V-NLI layer. The following seven stages are used: query interpretation, data transformation, visual mapping, view transformation, human interaction, dialogue management, and presentation. Finally, we also shed light on several promising directions for future work in the V-NLI community.
翻译:利用面向视觉的自然语言界面(V-NLI)作为直接操作视觉分析的一种补充投入模式,可以提供有吸引力的用户经验,使用户能够专注于他们的任务,而不必担心如何在界面上操作可视化工具。在过去20年中,利用先进的自然语言处理技术,在学术研究和商业软件中开发了许多V-NLI系统,特别是在最近几年里。在本篇文章中,我们对现有V-NLI系统进行了全面审查。为了对每份文件进行分类,我们根据V-NLI层扩展的经典信息可视化管道,开发了明确的维度。使用了以下七个阶段:查询解释、数据转换、视觉制图、观点转换、人际互动、对话管理和演示。最后,我们还为V-NLI社区的未来工作指明了一些有希望的方向。