Automatic Data Visualization Generation from Chinese Natural Language
  Questions
Yan Ge Victor Junqiu Wei Yuanfeng Song Jason Chen Zhang Raymond Chi-Wing Wong
Data visualization has emerged as an effective tool for getting insights from
massive datasets. Due to the hardness of manipulating the programming languages
of data visualization, automatic data visualization generation from natural
languages (Text-to-Vis) is becoming increasingly popular. Despite the plethora
of research effort on the English Text-to-Vis, studies have yet to be conducted
on data visualization generation from questions in Chinese. Motivated by this,
we propose a Chinese Text-to-Vis dataset in the paper and demonstrate our first
attempt to tackle this problem. Our model integrates multilingual BERT as the
encoder, boosts the cross-lingual ability, and infuses the $n$-gram information
into our word representation learning. Our experimental results show that our
dataset is challenging and deserves further research.