Good-Enough Compositional Data Augmentation

Understand

We propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models.

• Under this protocol, synthetic training examples are constructed by taking real training examples and replacing (possibly discontinuous) fragments with other fragments that appear in at least one similar environment.
• The protocol is model-agnostic and useful for a variety of tasks.
• Applied to neural sequence-to-sequence models, it reduces error rate by as much as 87% on diagnostic tasks from the SCAN dataset and 16% on a semantic parsing task.