@inproceedings{ge-etal-2024-automatic,
    title = "Automatic Data Visualization Generation from {C}hinese Natural Language Questions",
    author = "Ge, Yan  and
      Wei, Victor Junqiu  and
      Song, Yuanfeng  and
      Zhang, Jason Chen  and
      Wong, Raymond Chi-Wing",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.169",
    pages = "1889--1898",
    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.",
}

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.
Ge, Yan Wei, Victor Junqiu Song, Yuanfeng Zhang, Jason Chen Wong, Raymond Chi-Wing
Automatic Data Visualization Generation from Chinese Natural Language Questions