AutoChart: A Dataset for Chart-to-Text Generation Task

Jiawen Zhu, Jinye Ran, Roy Ka-Wei Lee, Zhi Li, Kenny Choo


Abstract
The analytical description of charts is an exciting and important research area with many applications in academia and industry. Yet, this challenging task has received limited attention from the computational linguistics research community. This paper proposes AutoChart, a large dataset for the analytical description of charts, which aims to encourage more research into this important area. Specifically, we offer a novel framework that generates the charts and their analytical description automatically. We conducted extensive human and machine evaluation on the generated charts and descriptions and demonstrate that the generated texts are informative, coherent, and relevant to the corresponding charts.
Anthology ID:
2021.ranlp-1.183
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1636–1644
Language:
URL:
https://aclanthology.org/2021.ranlp-1.183
DOI:
Bibkey:
Cite (ACL):
Jiawen Zhu, Jinye Ran, Roy Ka-Wei Lee, Zhi Li, and Kenny Choo. 2021. AutoChart: A Dataset for Chart-to-Text Generation Task. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1636–1644, Held Online. INCOMA Ltd..
Cite (Informal):
AutoChart: A Dataset for Chart-to-Text Generation Task (Zhu et al., RANLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.ranlp-1.183.pdf
Data
AutoChart