  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.
