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Rapidly creating effective visualizations using expressive grammars ischallenging for users who have limited time and limited skills in statisticsand data visualization. Even high-level, dedicated visualization tools oftenrequire users to manually select among data attributes, decide whichtransformations to apply, and specify mappings between visual encodingvariables and raw or transformed attributes.  In this paper we introduce Data2Vis, a neural translation model forautomatically generating visualizations from given datasets. We formulatevisualization generation as a sequence to sequence translation problem wheredata specifications are mapped to visualization specifications in a declarativelanguage (Vega-Lite). To this end, we train a multilayered attention-basedrecurrent neural network (RNN) with long short-term memory (LSTM) units on acorpus of visualization specifications.  Qualitative results show that our model learns the vocabulary and syntax fora valid visualization specification, appropriate transformations (count, bins,mean) and how to use common data selection patterns that occur within datavisualizations. Data2Vis generates visualizations that are comparable tomanually-created visualizations in a fraction of the time, with potential tolearn more complex visualization strategies at scale.

Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks