Automatic Data Visualization Generation from Chinese Natural Language Questions

Yan Ge, Victor Junqiu Wei, Yuanfeng Song, Jason Chen Zhang, Raymond Chi-Wing Wong

Abstract
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.
Anthology ID:
2024.lrec-main.169
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
1889–1898
Language:
URL:
https://aclanthology.org/2024.lrec-main.169
DOI:
Bibkey:
Cite (ACL):
Yan Ge, Victor Junqiu Wei, Yuanfeng Song, Jason Chen Zhang, and Raymond Chi-Wing Wong. 2024. Automatic Data Visualization Generation from Chinese Natural Language Questions. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 1889–1898, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Automatic Data Visualization Generation from Chinese Natural Language Questions (Ge et al., LREC-COLING 2024)
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https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.169.pdf
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