XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis

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Yingchaojie Feng, Xingbo Wang, Bo Pan, Kam Kwai Wong, Yi Ren, Shi Liu, Zihan Yan, Yuxin Ma, Huamin Qu, Wei Chen

 DOI: 10.1109/TVCG.2023.3240003

 Room: 109

2023-10-25T22:48:00ZGMT-0600Change your timezone on the schedule page
2023-10-25T22:48:00Z

Exemplar figure, described by caption below — The user interface of XNLI consists of two views, including the Data View and the Query View. Users can select or upload the dataset and explore the data attributes in the Data View. Then, they can use Query View to enter natural language queries, analyze data via charts, understand and diagnose the NLI process through interactive widgets, and gain hint feedback for query revision.

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Keywords

Natural language interface;visual data analysis;explainability

Abstract

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.