Through a natural language interface (NLI) for exploratory visual analysis, users can directly "ask" analytical questions about the given tabular data. This process greatly improves user experience and lowers the technical barriers of data analysis. Existing techniques focus on generating a visualization from a concrete question. However, complex questions, requiring multiple data queries and visualizations to answer, are frequently asked in data exploration and analysis, which cannot be easily solved with the existing techniques. To address this issue, in this paper, we introduce Talk2Data, a natural language interface for exploratory visual analysis that supports answering complex questions. It leverages an advanced deep-learning model to resolve complex questions into a series of simple questions that could gradually elaborate on the users' requirements. To present answers, we design a set of annotated and captioned visualizations to represent the answers in a form that supports interpretation and narration. We conducted an ablation study and a controlled user study to evaluate Talk2Data's effectiveness and usefulness.

翻译：通过面向探索性可视化分析的自然语言接口，用户可直接对给定表格数据"提出"分析性问题。该过程显著提升了用户体验，降低了数据分析的技术门槛。现有技术主要聚焦于从具体问题生成可视化结果。然而，在数据探索与分析过程中，用户频繁提出的复杂问题往往需要多次数据查询与多个可视化结果才能作答，现有技术难以有效处理此类问题。针对这一挑战，本文提出Talk2Data——一种支持回答复杂问题的探索性可视化分析自然语言接口。该接口利用先进深度学习模型将复杂问题分解为一系列能逐步明晰用户需求的简单问题。为呈现答案，我们设计了带注释与说明的可视化方案，使答案能以兼具解释性与叙事性的形式呈现。通过消融实验和受控用户研究，我们验证了Talk2Data的有效性与实用性。