Quda：用于可视化数据分析的自然语言建模
Quda: Natural Language Queries for Visual Data Analytics
摘要：
从自由文本中识别分析任务对于面向可视化的自然语言接口（V-NLI）提出有效的可视化是至关重要的。然而，由于人类语言的模糊性和复杂性，它是具有挑战性的。为了应对这一挑战，我们提出了一个新的数据集，称为Quda，旨在帮助V-NLI通过训练和评估尖端的多标签分类模型，从自由形式的自然语言中识别分析任务。我们的数据集包含$14,035$不同的用户查询，每个查询都有一个或多个分析任务的注释。我们实现这一目标，首先收集种子查询与数据分析，然后采用广泛的群众力量的释义生成和验证。我们通过三个应用程序证明了Quda的有用性。本研究首次尝试构建一个大规模的语料库来识别分析任务。随着Quda的发布，我们希望它将推动V-NLI在数据分析和可视化方面的研究和开发。
英文摘要：
The identification of analytic tasks from free text is critical for visualization-oriented natural language interfaces (V-NLIs) to suggest effective visualizations. However, it is challenging due to the ambiguity and complexity nature of human language. To address this challenge, we present a new dataset, called Quda, that aims to help V-NLIs recognize analytic tasks from free-form natural language by training and evaluating cutting-edge multi-label classification models. Our dataset contains $14,035$ diverse user queries, and each is annotated with one or multiple analytic tasks. We achieve this goal by first gathering seed queries with data analysts and then employing extensive crowd force for paraphrase generation and validation. We demonstrate the usefulness of Quda through three applications. This work is the first attempt to construct a large-scale corpus for recognizing analytic tasks. With the release of Quda, we hope it will boost the research and development of V-NLIs in data analysis and visualization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
