Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example
Mukul Singh, Jose Cambronero, Sumit Gulwani, Vu Le, Carina Negreanu,, Gust Verbruggen

TL;DR
CORNET is a system that learns spreadsheet conditional formatting rules from user-provided examples, combining symbolic and neural methods to assist users in automatically generating formatting rules within Excel.
Contribution
It introduces a novel approach that integrates symbolic rule enumeration with neural ranking to automatically learn formatting rules from minimal user examples.
Findings
Successfully generates accurate formatting rules from few examples
Integrates symbolic and neural methods for rule learning
Demonstrated as an Excel add-in with practical usability
Abstract
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…
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Taxonomy
TopicsStatistics Education and Methodologies · Spreadsheets and End-User Computing
