Rapidly creating effective visualizations using expressive grammars is challenging for users who have limited time and limited skills in statistics and data visualization. Even high-level, dedicated visualization tools often require users to manually select among data attributes, decide which transformations to apply, and specify mappings between visual encoding variables and raw or transformed attributes. In this paper, we introduce Data2Vis, a neural translation model, for automatically generating visualizations from given datasets. We formulate visualization generation as a sequence to sequence translation problem where data specification is mapped to a visualization specification in a declarative language (Vega-Lite). To this end, we train a multilayered Long Short-Term Memory (LSTM) model with attention on a corpus of visualization specifications. Qualitative results show that our model learns the vocabulary and syntax for a valid visualization specification, appropriate transformations (count, bins, mean) and how to use common data selection patterns that occur within data visualizations. Our model generates visualizations that are comparable to manually-created visualizations in a fraction of the time, with potential to learn more complex visualization strategies at scale.

本文介绍了Data2Vis：一种神经翻译模型，可自动生成给定数据集的可视化。通过将数据规范映射到声明语言（Vega-Lite）中的可视化规范，将可视化生成作为序列到序列翻译问题。为此，我们训练了一个多层基于注意力的长期短期记忆（LSTM）单元的循环神经网络（RNN）模型在可视化规范语料库上，以生成与手动创建的可视化相媲美但时间仅为其一小部分的可视化。

Data2Vis: 序列到序列循环神经网络自动生成数据可视化