Natural language interfaces (NLIs) have shown great promise for visual data analysis, allowing people to flexibly specify and interact with visualizations. However, developing visualization NLIs remains a challenging task, requiring low-level implementation of natural language processing (NLP) techniques as well as knowledge of visual analytic tasks and visualization design.
We present NL4DV, a toolkit for natural language-driven data visualization. NL4DV is a Python package that takes as input a tabular dataset and a natural language query about that dataset. In response, the toolkit returns an analytic specification modeled as a JSON object containing data attributes, analytic tasks, and a list of Vega-Lite specifications relevant to the input query.
In doing so, NL4DV aids visualization developers who may not have a background in NLP, enabling them to create new visualization NLIs or incorporate natural language input within their existing systems. We demonstrate NL4DV's usage and capabilities through four examples:
- rendering visualizations using natural language in a Jupyter notebook,
- developing an NLI to specify and edit Vega-Lite charts,
- recreating data ambiguity widgets from the DataTone system, and
- incorporating speech input to create a multimodal visualization system.
At the Information Interfaces Lab, computing technologies are developed that help people take advantage of information to enrich their lives. The lab group develops ways to help people understand information via user interface design, information visualization, peripheral awareness techniques and embodied agents. The goal is to help people make better judgments by learning from all the information available to them.
