SpreadsheetCoder
SpreadsheetCoder
**SpreadsheetCoder** is a neural network architecture for spreadsheet formula prediction. It is a [BERT](https://paperswithcode.com/method/bert)-based model architecture to represent the tabular context in both row-based and column-based formats. A [BERT](https://paperswithcode.com/method/bert) encoder computes an embedding vector for each input token, incorporating the contextual information from nearby rows and columns. The BERT encoder is initialized from the weights pre-trained on English text corpora, which is beneficial for encoding table headers. To handle cell references, a two-stage decoding process is used inspired by sketch learning for program synthesis. The decoder first generates a formula sketch, which does not include concrete cell references, and then predicts the corresponding cell ranges to generate the complete formula
SpreadsheetCoder: Formula Prediction from Semi-structured Context
https://paperswithcode.com/paper/spreadsheetcoder-formula-prediction-from-semi-1
SpreadsheetCoder: Formula Prediction from Semi-structured Context