Improving Compositional Generalization with Latent Structure and Data
Augmentation
Improving Compositional Generalization with Latent Structure and Data
Augmentation
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 …