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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Natural Language to Visualization by Neural Machine Translation.

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    This study introduces ncNet, a Transformer-based model for natural language to visualization (NL2VIS) translation. ncNet simplifies data visualization creation by accurately converting natural language queries into charts, even with complex data transformations.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Artificial Intelligence

    Background:

    • Natural Language to Visualization (NL2VIS) translation aims to simplify data visualization creation.
    • Current NL2VIS methods rely on semantic parsers and heuristic algorithms, limiting their ability to handle complex data transformations.
    • Deep neural networks, particularly Transformer models, show promise for advancing NL2VIS capabilities.

    Purpose of the Study:

    • To present ncNet, a Transformer-based sequence-to-sequence model for NL2VIS.
    • To incorporate visualization-aware optimizations for improved learning and rendering.
    • To enhance natural language comprehension by accepting optional chart templates as input.

    Main Methods:

    • Developed ncNet, a Transformer-based sequence-to-sequence model.
    • Implemented visualization-aware optimizations, including attention-forcing and visualization-aware rendering.
    • Integrated optional chart templates to constrain visualization possibilities.

    Main Results:

    • ncNet demonstrated good accuracy on the nvBench benchmark.
    • Quantitative evaluation and user studies confirmed ncNet's effectiveness.
    • The model proved to be user-friendly for generating data visualizations from natural language queries.

    Conclusions:

    • ncNet offers an effective end-to-end solution for NL2VIS.
    • The model's visualization-aware optimizations enhance accuracy and usability.
    • ncNet facilitates easier data visualization creation for a wider audience.