  Generic unstructured neural networks have been shown to struggle on
out-of-distribution compositional generalization. Compositional data
augmentation via example recombination has transferred some prior knowledge
about compositionality to such black-box neural models for several semantic
parsing tasks, but this often required task-specific engineering or provided
limited gains.
  We present a more powerful data recombination method using a model called
Compositional Structure Learner (CSL). CSL is a generative model with a
quasi-synchronous context-free grammar backbone, which we induce from the
training data. We sample recombined examples from CSL and add them to the
fine-tuning data of a pre-trained sequence-to-sequence model (T5). This
procedure effectively transfers most of CSL's compositional bias to T5 for
diagnostic tasks, and results in a model even stronger than a T5-CSL ensemble
on two real world compositional generalization tasks. This results in new
state-of-the-art performance for these challenging semantic parsing tasks
requiring generalization to both natural language variation and novel
compositions of elements.
