PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context

Xinyun Chen, Linyuan Gong, Alvin Cheung, Dawn Song


Abstract
Creating effective visualization is an important part of data analytics. While there are many libraries for creating visualization, writing such code remains difficult given the myriad of parameters that users need to provide. In this paper, we propose the new task of synthesizing visualization programs from a combination of natural language utterances and code context. To tackle the learning problem, we introduce PlotCoder, a new hierarchical encoder-decoder architecture that models both the code context and the input utterance. We use PlotCoder to first determine the template of the visualization code, followed by predicting the data to be plotted. We use Jupyter notebooks containing visualization programs crawled from GitHub to train PlotCoder. On a comprehensive set of test samples from those notebooks, we show that PlotCoder correctly predicts the plot type of about 70% samples, and synthesizes the correct programs for 35% samples, performing 3-4.5% better than the baselines.
Anthology ID:
2021.acl-long.169
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2169–2181
Language:
URL:
https://aclanthology.org/2021.acl-long.169
DOI:
10.18653/v1/2021.acl-long.169
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.169.pdf