Towards Natural Language-Based Visualization Authoring

TL;DR

This paper introduces a natural language interface pipeline for visualization authoring that simplifies user interaction by translating natural language into executable editing actions, enabling more accessible visualization creation.

Contribution

It proposes a structured representation of user intents, a deep learning-based NL interpreter, and demonstrates its application in tools like Excel chart editor and VisTalk.

Findings

User study reveals natural language as an effective way to author charts.

The NL interpreter is reusable and adaptable across different visualization tools.

Natural language can significantly improve visualization authoring usability.

Abstract

A key challenge to visualization authoring is the process of getting familiar with the complex user interfaces of authoring tools. Natural Language Interface (NLI) presents promising benefits due to its learnability and usability. However, supporting NLIs for authoring tools requires expertise in natural language processing, while existing NLIs are mostly designed for visual analytic workflow. In this paper, we propose an authoring-oriented NLI pipeline by introducing a structured representation of users' visualization editing intents, called editing actions, based on a formative study and an extensive survey on visualization construction tools. The editing actions are executable, and thus decouple natural language interpretation and visualization applications as an intermediate layer. We implement a deep learning-based NL interpreter to translate NL utterances into editing actions. The interpreter is reusable and extensible across authoring tools. The authoring tools only need to map the editing actions into tool-specific operations. To illustrate the usages of the NL interpreter, we implement an Excel chart editor and a proof-of-concept authoring tool, VisTalk. We conduct a user study with VisTalk to understand the usage patterns of NL-based authoring systems. Finally, we discuss observations on how users author charts with natural language, as well as implications for future research.