CARTE: pretraining and transfer for tabular learning
Myung Jun Kim Léo Grinsztajn Gaël Varoquaux
Pretrained deep-learning models are the go-to solution for images or text.
However, for tabular data the standard is still to train tree-based models.
Pre-training or transfer is a huge challenge as in general tables have columns
about different quantities and naming conventions that vary vastly across
sources. Data integration tackles correspondences across multiple sources:
schema matching for columns, and entity matching for entries. We propose a
neural architecture that does not need such matches. As a result, we can
pretrain it on background data that has not been matched. The architecture -
CARTE for Context Aware Representation of Table Entries - uses a graph
representation of tabular (or relational) data to process tables with different
columns, string embeddings of entries and columns names to model an open
vocabulary, and a graph-attentional network to contextualize entries with
column names and neighboring entries. An extensive benchmark shows that CARTE
facilitates learning, outperforming a solid set of baselines including the best
tree-based models. CARTE also enables joint learning across tables with
unmatched columns, enhancing a small table with bigger ones. CARTE opens the
door to large pretrained models embarking information for tabular data.