  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.
