@inproceedings{zhu-etal-2021-autochart,
title = "{A}uto{C}hart: A Dataset for Chart-to-Text Generation Task",
author = "Zhu, Jiawen and
Ran, Jinye and
Lee, Roy Ka-Wei and
Li, Zhi and
Choo, Kenny",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.183/",
pages = "1636--1644",
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."
}
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%0 Conference Proceedings
%T AutoChart: A Dataset for Chart-to-Text Generation Task
%A Zhu, Jiawen
%A Ran, Jinye
%A Lee, Roy Ka-Wei
%A Li, Zhi
%A Choo, Kenny
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F zhu-etal-2021-autochart
%X 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.
%U https://aclanthology.org/2021.ranlp-1.183/
%P 1636-1644
Markdown (Informal)
[AutoChart: A Dataset for Chart-to-Text Generation Task](https://aclanthology.org/2021.ranlp-1.183/) (Zhu et al., RANLP 2021)
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..