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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 barriersof data analysis. Existing techniques focus on generating a visualization froma concrete question. However, complex questions, requiring multiple dataqueries and visualizations to answer, are frequently asked in data explorationand analysis, which cannot be easily solved with the existing techniques. Toaddress this issue, in this paper, we introduce Talk2Data, a natural languageinterface for exploratory visual analysis that supports answering complexquestions. It leverages an advanced deep-learning model to resolve complexquestions into a series of simple questions that could gradually elaborate onthe users' requirements. To present answers, we design a set of annotated andcaptioned visualizations to represent the answers in a form that supportsinterpretation and narration. We conducted an ablation study and a controlleduser study to evaluate Talk2Data's effectiveness and usefulness.

Talk2Data: A Natural Language Interface for Exploratory Visual Analysis via Question Decomposition