Data management and analysis tasks are often carried out using spreadsheet software. A popular feature in most spreadsheet platforms is the ability to define data-dependent formatting rules. These rules can express actions such as "color red all entries in a column that are negative" or "bold all rows not containing error or failure." Unfortunately, users who want to exercise this functionality need to manually write these conditional formatting (CF) rules. We introduce CORNET, a system that automatically learns such conditional formatting rules from user examples. CORNET takes inspiration from inductive program synthesis and combines symbolic rule enumeration, based on semi-supervised clustering and iterative decision tree learning, with a neural ranker to produce accurate conditional formatting rules. In this demonstration, we show CORNET in action as a simple add-in to Microsoft Excel. After the user provides one or two formatted cells as examples, CORNET generates formatting rule suggestions for the user to apply to the spreadsheet.

翻译：数据管理与分析任务通常借助电子表格软件完成。大多数电子表格平台都具备定义数据依赖型格式规则的功能，例如"将列中所有负值标记为红色"或"加粗所有不含错误或失败信息的行"。然而，用户若需使用该功能，必须手动编写条件格式规则。我们提出CORNET系统——一种能从用户示例中自动学习条件格式规则的框架。CORNET受归纳式程序综合启发，通过结合基于半监督聚类与迭代决策树学习的符号规则枚举机制，并辅以神经排序器，生成精确的条件格式规则。在本演示中，我们展示了CORNET作为Microsoft Excel简易插件的工作流程：用户提供一至两个已格式化单元格作为示例后，CORNET即可生成格式规则建议供用户应用于整个电子表格。