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Spreadsheet formula prediction has been an important program synthesisproblem with many real-world applications. Previous works typically utilizeinput-output examples as the specification for spreadsheet formula synthesis,where each input-output pair simulates a separate row in the spreadsheet.However, this formulation does not fully capture the rich context in real-worldspreadsheets. First, spreadsheet data entries are organized as tables, thusrows and columns are not necessarily independent from each other. In addition,many spreadsheet tables include headers, which provide high-level descriptionsof the cell data. However, previous synthesis approaches do not considerheaders as part of the specification. In this work, we present the firstapproach for synthesizing spreadsheet formulas from tabular context, whichincludes both headers and semi-structured tabular data. In particular, wepropose SpreadsheetCoder, a BERT-based model architecture to represent thetabular context in both row-based and column-based formats. We train our modelon a large dataset of spreadsheets, and demonstrate that SpreadsheetCoderachieves top-1 prediction accuracy of 42.51%, which is a considerableimprovement over baselines that do not employ rich tabular context. Compared tothe rule-based system, SpreadsheetCoder assists 82% more users in composingformulas on Google Sheets.

SpreadsheetCoder: Formula Prediction from Semi-structured Context