Natural Language Models for Data Visualization Utilizing nvBench Dataset
This work addresses the challenge of making data visualization more accessible through natural language interfaces, but it is incremental as it builds on existing methods for similar tasks like SQL translation.
The paper tackles the problem of translating natural language queries into data visualization commands using models like BERT and T5, achieving performance evaluated on the nvBench dataset.
Translation of natural language into syntactically correct commands for data visualization is an important application of natural language models and could be leveraged to many different tasks. A closely related effort is the task of translating natural languages into SQL queries, which in turn could be translated into visualization with additional information from the natural language query supplied\cite{Zhong:2017qr}. Contributing to the progress in this area of research, we built natural language translation models to construct simplified versions of data and visualization queries in a language called Vega Zero. In this paper, we explore the design and performance of these sequence to sequence transformer based machine learning model architectures using large language models such as BERT as encoders to predict visualization commands from natural language queries, as well as apply available T5 sequence to sequence models to the problem for comparison.