{"id":682191,"date":"2020-08-03T20:40:17","date_gmt":"2020-08-04T03:40:17","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=682191"},"modified":"2020-10-20T23:28:07","modified_gmt":"2020-10-21T06:28:07","slug":"learning-formatting-style-transfer-and-structure-extraction-for-spreadsheet-tables-with-a-hybrid-neural-network-architecture","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-formatting-style-transfer-and-structure-extraction-for-spreadsheet-tables-with-a-hybrid-neural-network-architecture\/","title":{"rendered":"Learning Formatting Style Transfer and Structure Extraction for Spreadsheet Tables with a Hybrid Neural Network Architecture"},"content":{"rendered":"<div style=\"width: 640px;\" class=\"wp-video\"><!--[if lt IE 9]><script>document.createElement('video');<\/script><![endif]-->\n<video class=\"wp-video-shortcode\" id=\"video-682191-1\" width=\"640\" height=\"360\" preload=\"metadata\" controls=\"controls\"><source type=\"video\/mp4\" src=\"https:\/\/raw.githubusercontent.com\/hadong12347\/TableStyleTransfer\/master\/TableStyleTransferDemoVideo.mp4?_=1\" \/><a href=\"https:\/\/raw.githubusercontent.com\/hadong12347\/TableStyleTransfer\/master\/TableStyleTransferDemoVideo.mp4\">https:\/\/raw.githubusercontent.com\/hadong12347\/TableStyleTransfer\/master\/TableStyleTransferDemoVideo.mp4<\/a><\/video><\/div>\n<p>&nbsp;<\/p>\n<p>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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>https:\/\/raw.githubusercontent.com\/hadong12347\/TableStyleTransfer\/master\/TableStyleTransferDemoVideo.mp4 &nbsp; 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 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