  In tasks like semantic parsing, instruction following, and question
answering, standard deep networks fail to generalize compositionally from small
datasets. Many existing approaches overcome this limitation with model
architectures that enforce a compositional process of sentence interpretation.
In this paper, we present a domain-general and model-agnostic formulation of
compositionality as a constraint on symmetries of data distributions rather
than models. Informally, we prove that whenever a task can be solved by a
compositional model, there is a corresponding data augmentation scheme -- a
procedure for transforming examples into other well formed examples -- that
imparts compositional inductive bias on any model trained to solve the same
task. We describe a procedure called LEXSYM that discovers these
transformations automatically, then applies them to training data for ordinary
neural sequence models. Unlike existing compositional data augmentation
procedures, LEXSYM can be deployed agnostically across text, structured data,
and even images. It matches or surpasses state-of-the-art, task-specific models
on COGS semantic parsing, SCAN and ALCHEMY instruction following, and
CLEVR-COGENT visual question answering datasets.

Computation and Language;Computer Vision and Pattern Recognition;Machine Learning