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.
BibTeX
@inproceedings{chen-etal-2021-plotcoder,
title = "{P}lot{C}oder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context",
author = "Chen, Xinyun and
Gong, Linyuan and
Cheung, Alvin and
Song, Dawn",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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 = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.169/",
doi = "10.18653/v1/2021.acl-long.169",
pages = "2169--2181"
}