@inproceedings{liu-etal-2021-haconvgnn-hierarchical,
    title = "{HAC}onv{GNN}: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in {J}upyter Notebooks",
    author = "Liu, Xuye  and
      Wang, Dakuo  and
      Wang, April  and
      Hou, Yufang  and
      Wu, Lingfei",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.findings-emnlp.381/",
    doi = "10.18653/v1/2021.findings-emnlp.381",
    pages = "4473--4485",
    abstract = "Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells. In this paper, we propose a new task of code documentation generation (CDG) for computational notebooks. In contrast to the previous CDG tasks which focus on generating documentation for single code snippets, in a computational notebook, one documentation in a markdown cell often corresponds to multiple code cells, and these code cells have an inherent structure. We proposed a new model (HAConvGNN) that uses a hierarchical attention mechanism to consider the relevant code cells and the relevant code tokens information when generating the documentation. Tested on a new corpus constructed from well-documented Kaggle notebooks, we show that our model outperforms other baseline models."
}