  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.
