The Experts below are selected from a list of 3147843 Experts worldwide ranked by ideXlab platform

Vinicius Maracaja-coutinho - One of the best experts on this subject based on the ideXlab platform.

Dongmei Zhang - One of the best experts on this subject based on the ideXlab platform.

  • learning formatting style transfer and structure extraction for spreadsheet tables with a hybrid neural network architecture
    Conference on Information and Knowledge Management, 2020
    Co-Authors: Haoyu Dong, Jiong Yang, Shi Han, Dongmei Zhang
    Abstract:

    document.createElement('video'); https://raw.githubusercontent.com/hadong12347/TableStyleTransfer/master/TableStyleTransferDemoVideo.mp4 Table formatting is a typical task for spreadsheet users to better exhibit table structures and data relationships. But quickly and effectively formatting tables is a challenge for users. Lots of manual operations are needed, especially for complex tables. In this paper, we propose techniques for table formatting style transfer, i.e., to automatically format a target table according to the style of a reference table. Considering the latent many-to-many mappings between table structures and formats, we propose CellNet, which is a novel end-to-end, multi-task model leveraging conditional Generative Adversarial Networks (cGANs) with three key components to (1) model and recognize table structures; (2) encode formatting styles; (3) learn and apply the latent mapping based on recognized table structure and encoded style, respectively. Moreover, we build up a spreadsheet table corpus containing 5,226 tables with high-quality formats and 784 tables with human-labeled structures. Our evaluation shows that CellNet is highly effective according to both quantitative metrics and human perception studies by comparing with heuristic-based and other learning-based methods.

  • learning formatting style transfer and structure extraction for spreadsheet tables with a hybrid neural network architecture
    Conference on Information and Knowledge Management, 2020
    Co-Authors: Haoyu Dong, Jiong Yang, Shi Han, Dongmei Zhang
    Abstract:

    Table formatting is a typical task for spreadsheet users to better exhibit table structures and data relationships. But quickly and effectively formatting tables is a challenge for users. Lots of manual operations are needed, especially for complex tables. In this paper, we propose techniques for table formatting style transfer, i.e., to automatically format a target table according to the style of a reference table. Considering the latent many-to-many mappings between table structures and formats, we propose CellNet, which is a novel end-to-end, multi-task model leveraging conditional Generative Adversarial Networks (cGANs) with three key components to (1) model and recognize table structures; (2) encode formatting styles; (3) learn and apply the latent mapping based on recognized table structure and encoded style, respectively. Moreover, we build up a spreadsheet table corpus containing 5,226 tables with high-quality formats and 784 tables with human-labeled structures. Our evaluation shows that CellNet is highly effective according to both quantitative metrics and human perception studies by comparing with heuristic-based and other learning-based methods.

Raúl Arias-carrasco - One of the best experts on this subject based on the ideXlab platform.

Scientific Data Curation Team - One of the best experts on this subject based on the ideXlab platform.

Jeffrey Kidd - One of the best experts on this subject based on the ideXlab platform.