@inproceedings{he-etal-2023-hermes,
    title = "{H}erm{E}s: Interactive Spreadsheet Formula Prediction via Hierarchical Formulet Expansion",
    author = "He, Wanrong  and
      Dong, Haoyu  and
      Gao, Yihuai  and
      Fan, Zhichao  and
      Guo, Xingzhuo  and
      Hou, Zhitao  and
      Lv, Xiao  and
      Jia, Ran  and
      Han, Shi  and
      Zhang, Dongmei",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.465",
    doi = "10.18653/v1/2023.acl-long.465",
    pages = "8356--8372",
    abstract = "We propose HermEs, the first approach for spreadsheet formula prediction via HiEraRchical forMulet ExpanSion, where hierarchical expansion means generating formulas following the underlying parse tree structure, and Formulet refers to commonly-used multi-level patterns mined from real formula parse trees. HermEs improves the formula prediction accuracy by (1) guaranteeing correct grammar by hierarchical generation rather than left-to-right generation and (2) significantly streamlining the token-level decoding with high-level Formulet. Notably, instead of generating formulas in a pre-defined fixed order, we propose a novel sampling strategy to systematically exploit a variety of hierarchical and multi-level expansion orders and provided solid mathematical proof, with the aim of meeting diverse human needs of the formula writing order in real applications. We further develop an interactive formula completion interface based on HermEs, which shows a new user experience in \url{https://github.com/formulet/HERMES}.",
}

We propose HermEs, the first approach for spreadsheet formula prediction via HiEraRchical forMulet ExpanSion, where hierarchical expansion means generating formulas following the underlying parse tree structure, and Formulet refers to commonly-used multi-level patterns mined from real formula parse trees. HermEs improves the formula prediction accuracy by (1) guaranteeing correct grammar by hierarchical generation rather than left-to-right generation and (2) significantly streamlining the token-level decoding with high-level Formulet. Notably, instead of generating formulas in a pre-defined fixed order, we propose a novel sampling strategy to systematically exploit a variety of hierarchical and multi-level expansion orders and provided solid mathematical proof, with the aim of meeting diverse human needs of the formula writing order in real applications. We further develop an interactive formula completion interface based on HermEs, which shows a new user experience in \url{https://github.com/formulet/HERMES}.
He, Wanrong Dong, Haoyu Gao, Yihuai Fan, Zhichao Guo, Xingzhuo Hou, Zhitao Lv, Xiao Jia, Ran Han, Shi Zhang, Dongmei
HermEs: Interactive Spreadsheet Formula Prediction via Hierarchical Formulet Expansion