  We propose an approach to manipulate existing interactive visualizations to
answer users' natural language queries. We analyze the natural language tasks
and propose a design space of a hierarchical task structure, which allows for a
systematic decomposition of complex queries. We introduce a four-level
visualization manipulation space to facilitate in-situ manipulations for
visualizations, enabling a fine-grained control over the visualization
elements. Our methods comprise two essential components: the natural
language-to-task translator and the visualization manipulation parser. The
natural language-to-task translator employs advanced NLP techniques to extract
structured, hierarchical tasks from natural language queries, even those with
varying degrees of ambiguity. The visualization manipulation parser leverages
the hierarchical task structure to streamline these tasks into a sequence of
atomic visualization manipulations. To illustrate the effectiveness of our
approach, we provide real-world examples and experimental results. The
evaluation highlights the precision of our natural language parsing
capabilities and underscores the smooth transformation of visualization
manipulations.
