XNLI: Explaining and Diagnosing NLI-Based Visual Data Analysis

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 us...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2024-07, Vol.30 (7), p.3813-3827
Main Authors: Feng, Yingchaojie, Wang, Xingbo, Pan, Bo, Wong, Kam Kwai, Ren, Yi, Liu, Shi, Yan, Zihan, Ma, Yuxin, Qu, Huamin, Chen, Wei
Format: Article
Language:English
Subjects:
Data analysis
Data visualization
Encoding
explainability
Motion pictures
Natural language interface
Prototypes
Queries
Scientific visualization
Task analysis
visual data analysis
Visualization
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Summary: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.
ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2023.3240003