  Data preparation, also called data wrangling, is considered one of the most
expensive and time-consuming steps when performing analytics or building
machine learning models. Preparing data typically involves collecting and
merging data from complex heterogeneous, and often large-scale data sources,
such as data lakes. In this paper, we introduce a novel approach toward
automatic data wrangling in an attempt to alleviate the effort of end-users,
e.g. data analysts, in structuring dynamic views from data lakes in the form of
tabular data. We aim to address table augmentation tasks, including row/column
population and data imputation. Given a corpus of tables, we propose a
retrieval augmented self-trained transformer model. Our self-learning strategy
consists in randomly ablating tables from the corpus and training the
retrieval-based model to reconstruct the original values or headers given the
partial tables as input. We adopt this strategy to first train the dense neural
retrieval model encoding table-parts to vectors, and then the end-to-end model
trained to perform table augmentation tasks. We test on EntiTables, the
standard benchmark for table augmentation, as well as introduce a new benchmark
to advance further research: WebTables. Our model consistently and
substantially outperforms both supervised statistical methods and the current
state-of-the-art transformer-based models.
