SpreadsheetCoder is a neural network architecture for spreadsheet formula prediction. It is a BERT-based model architecture to represent the tabular context in both row-based and column-based formats. A 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
Source: SpreadsheetCoder: Formula Prediction from Semi-structured ContextPaper | Code | Results | Date | Stars |
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BERT
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Language Models | |
Convolution
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Convolutions | |
LSTM
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Recurrent Neural Networks | |
Residual Connection
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