DataVisT5: A Pre-trained Language Model for Jointly Understanding Text and Data Visualization
<|reference_start|>DataVisT5: A Pre-trained Language Model for Jointly Understanding Text and Data Visualization: Data visualization (DV) is the fundamental and premise tool to improve the efficiency in conveying the insights behind the big data, which has been widely accepted in existing data-driven world. Task automation in DV, such as converting natural language queries to visualizations (i.e., text-to-vis), generating explanations from visualizations (i.e., vis-to-text), answering DV-related questions in free form (i.e. FeVisQA), and explicating tabular data (i.e., table-to-text), is vital for advancing the field. Despite their potential, the application of pre-trained language models (PLMs) like T5 and BERT in DV has been limited by high costs and challenges in handling cross-modal information, leading to few studies on PLMs for DV. We introduce \textbf{DataVisT5}, a novel PLM tailored for DV that enhances the T5 architecture through a hybrid objective pre-training and multi-task fine-tuning strategy, integrating text and DV datasets to effectively interpret cross-modal semantics. Extensive evaluations on public datasets show that DataVisT5 consistently outperforms current state-of-the-art models on various DV-related tasks. We anticipate that DataVisT5 will not only inspire further research on vertical PLMs but also expand the range of applications for PLMs.<|reference_end|>
@article{wan2024datavist5:,
    title={DataVisT5: A Pre-trained Language Model for Jointly Understanding Text
  and Data Visualization},
    author={Zhuoyue Wan, Yuanfeng Song, Shuaimin Li, Chen Jason Zhang, Raymond
  Chi-Wing Wong},
    journal={arXiv preprint arXiv:2408.07401},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07401},
    primaryClass={cs.CL cs.AI cs.DB}
}