  Conventional text-to-SQL studies are limited to a single task with a
fixed-size training and test set. When confronted with a stream of tasks common
in real-world applications, existing methods struggle with the problems of
insufficient supervised data and high retraining costs. The former tends to
cause overfitting on unseen databases for the new task, while the latter makes
a full review of instances from past tasks impractical for the model, resulting
in forgetting of learned SQL structures and database schemas. To address the
problems, this paper proposes integrating semi-supervised learning (SSL) and
continual learning (CL) in a stream of text-to-SQL tasks and offers two
promising solutions in turn. The first solution Vanilla is to perform
self-training, augmenting the supervised training data with predicted
pseudo-labeled instances of the current task, while replacing the full volume
retraining with episodic memory replay to balance the training efficiency with
the performance of previous tasks. The improved solution SFNet takes advantage
of the intrinsic connection between CL and SSL. It uses in-memory past
information to help current SSL, while adding high-quality pseudo instances in
memory to improve future replay. The experiments on two datasets shows that
SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple
metrics.
