NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries
* Equal contribution
IEEE TVCG · 2021
Abstract
Natural language interfaces (NLls) 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: 1) rendering visualizations using natural language in a Jupyter notebook, 2) developing a NLI to specify and edit Vega-Lite charts, 3) recreating data ambiguity widgets from the DataTone system, and 4) incorporating speech input to create a multimodal visualization system.
Citation
@article{narechania2020nl4dv,
author = {Narechania, Arpit and Srinivasan, Arjun and Stasko, John},
title = {{NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries}},
journal = {IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
volume = {27},
number = {2},
pages = {369-379},
publisher = {IEEE},
doi = {10.1109/TVCG.2020.3030378},
url = {https://doi.org/10.1109/TVCG.2020.3030378}
}