Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations
arxiv(2024)
摘要
Spreadsheets are widely recognized as the most popular end-user programming
tools, which blend the power of formula-based computation, with an intuitive
table-based interface. Today, spreadsheets are used by billions of users to
manipulate tables, most of whom are neither database experts nor professional
programmers.
Despite the success of spreadsheets, authoring complex formulas remains
challenging, as non-technical users need to look up and understand non-trivial
formula syntax. To address this pain point, we leverage the observation that
there is often an abundance of similar-looking spreadsheets in the same
organization, which not only have similar data, but also share similar
computation logic encoded as formulas. We develop an Auto-Formula system that
can accurately predict formulas that users want to author in a target
spreadsheet cell, by learning and adapting formulas that already exist in
similar spreadsheets, using contrastive-learning techniques inspired by
"similar-face recognition" from compute vision.
Extensive evaluations on over 2K test formulas extracted from real enterprise
spreadsheets show the effectiveness of Auto-Formula over alternatives. Our
benchmark data is available at https://github.com/microsoft/Auto-Formula to
facilitate future research.
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