SpreadsheetCoder: Formula Prediction from Semi-structured Context
Abstract
Spreadsheet formula prediction has been an important
program synthesis problem with many
real-world applications. Previous works typically
utilize input-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-world spreadsheets.
First, spreadsheet data entries are organized
as tables, thus rows and columns are not necessarily
independent from each other. In addition,
many spreadsheet tables include headers, which
provide high-level descriptions of the cell data.
However, previous synthesis approaches do not
consider headers as part of the specification. In
this work, we present the first approach for synthesizing
spreadsheet formulas from tabular context,
which includes both headers and semi-structured
tabular data. In particular, we propose SpreadsheetCoder,
a BERT-based model architecture
to represent the tabular context in both row-based
and column-based formats. We train our model on
a large dataset of spreadsheets, and demonstrate
that SpreadsheetCoder achieves top-1 prediction
accuracy of 42:51%, which is a considerable
improvement over baselines that do not employ
rich tabular context. Compared to a rule-based
system, SpreadsheetCoder assists 82% more
users in composing formulas on Google Sheets.
program synthesis problem with many
real-world applications. Previous works typically
utilize input-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-world spreadsheets.
First, spreadsheet data entries are organized
as tables, thus rows and columns are not necessarily
independent from each other. In addition,
many spreadsheet tables include headers, which
provide high-level descriptions of the cell data.
However, previous synthesis approaches do not
consider headers as part of the specification. In
this work, we present the first approach for synthesizing
spreadsheet formulas from tabular context,
which includes both headers and semi-structured
tabular data. In particular, we propose SpreadsheetCoder,
a BERT-based model architecture
to represent the tabular context in both row-based
and column-based formats. We train our model on
a large dataset of spreadsheets, and demonstrate
that SpreadsheetCoder achieves top-1 prediction
accuracy of 42:51%, which is a considerable
improvement over baselines that do not employ
rich tabular context. Compared to a rule-based
system, SpreadsheetCoder assists 82% more
users in composing formulas on Google Sheets.
Research Areas
