  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.
