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    This study introduces a novel natural language interface (NLI) pipeline for visualization authoring. It simplifies chart creation by translating user language into executable editing actions, making tools more accessible.

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    Area of Science:

    • Human-Computer Interaction
    • Data Visualization
    • Natural Language Processing

    Background:

    • Visualization authoring tools often have complex interfaces, hindering usability.
    • Natural Language Interfaces (NLIs) offer potential for improved learnability and usability.
    • Existing NLIs are typically designed for visual analytics, not direct authoring workflows.

    Purpose of the Study:

    • To propose an authoring-oriented NLI pipeline for visualization tools.
    • To develop a structured representation of user visualization editing intents (editing actions).
    • To create a reusable and extensible natural language interpreter for visualization authoring.

    Main Methods:

    • Conducted a formative study and extensive survey on visualization construction tools.
    • Introduced 'editing actions' as a structured, executable representation of user intents.
    • Implemented a deep learning-based natural language interpreter.
    • Developed example authoring tools (Excel chart editor, VisTalk) and conducted a user study.

    Main Results:

    • The proposed pipeline decouples natural language interpretation from visualization applications via an intermediate layer.
    • The deep learning interpreter effectively translates natural language utterances into executable editing actions.
    • User study provided insights into natural language-based chart authoring patterns.

    Conclusions:

    • The developed NLI pipeline enhances visualization authoring by simplifying user interaction.
    • The intermediate 'editing actions' layer promotes reusability and extensibility across different authoring tools.
    • Findings inform future research on natural language interaction in data visualization.