{"id":682191,"date":"2020-08-03T20:40:17","date_gmt":"2020-08-04T03:40:17","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/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:\/\/cm-edgetun.pages.dev\/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\"><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 [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"CIKM'20 (applied research 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01:24:49","post_status":"publish","permalink":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/project\/spreadsheet-intelligence\/","post_excerpt":"At Microsoft Research Asia, this is the umbrella research project behind Ideas in Excel of Microsoft Office 365 product.\u00a0With successful technology transfers via close collaboration with Excel teams,\u00a0this intelligent\u00a0feature has been announced at Microsoft Ignite 2019 Conference and released with General Availability on March 1, 2019. There are following sub- or related research projects on some fundamental technology pillars, respectively. They jointly enable such one-click intelligence of Ideas in Excel. 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