  Natural language interfaces (NLIs) enable users to flexibly specify
analytical intentions in data visualization. However, diagnosing the
visualization results without understanding the underlying generation process
is challenging. Our research explores how to provide explanations for NLIs to
help users locate the problems and further revise the queries. We present XNLI,
an explainable NLI system for visual data analysis. The system introduces a
Provenance Generator to reveal the detailed process of visual transformations,
a suite of interactive widgets to support error adjustments, and a Hint
Generator to provide query revision hints based on the analysis of user queries
and interactions. Two usage scenarios of XNLI and a user study verify the
effectiveness and usability of the system. Results suggest that XNLI can
significantly enhance task accuracy without interrupting the NLI-based analysis
process.
