  Understanding the semantics of relational tables is instrumental for
automation in data exploration and preparation systems. A key source for
understanding a table is the semantics of its columns. With the rise of deep
learning, learned table representations are now available, which can be applied
for semantic type detection and achieve good performance on benchmarks.
Nevertheless, we observe a gap between this performance and its applicability
in practice. In this paper, we propose AdaTyper to address one of the most
critical deployment challenges: adaptation. AdaTyper uses weak-supervision to
adapt a hybrid type predictor towards new semantic types and shifted data
distributions at inference time, using minimal human feedback. The hybrid type
predictor of AdaTyper combines rule-based methods and a light machine learning
model for semantic column type detection. We evaluate the adaptation
performance of AdaTyper on real-world database tables hand-annotated with
semantic column types through crowdsourcing and find that the f1-score improves
for new and existing types. AdaTyper approaches an average precision of 0.6
after only seeing 5 examples, significantly outperforming existing adaptation
methods based on human-provided regular expressions or dictionaries.
