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Existing work on tabular representation learning jointly models tables andassociated text using self-supervised objective functions derived frompretrained language models such as BERT. While this joint pretraining improvestasks involving paired tables and text (e.g., answering questions abouttables), we show that it underperforms on tasks that operate over tableswithout any associated text (e.g., populating missing cells). We devise asimple pretraining objective (corrupt cell detection) that learns exclusivelyfrom tabular data and reaches the state-of-the-art on a suite of table basedprediction tasks. Unlike competing approaches, our model (TABBIE) providesembeddings of all table substructures (cells, rows, and columns), and it alsorequires far less compute to train. A qualitative analysis of our model'slearned cell, column, and row representations shows that it understands complextable semantics and numerical trends.

TABBIE: Pretrained Representations of Tabular Data