Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example

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 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.