Compositional Generalization for Primitive Substitutions

Understand

Compositional generalization is a basic mechanism in human language learning, but current neural networks lack such ability.

• In this paper, we conduct fundamental research for encoding compositionality in neural networks.
• Conventional methods use a single representation for the input sentence, making it hard to apply prior knowledge of compositionality.
• In contrast, our approach leverages such knowledge with two representations, one generating attention maps, and the other mapping attended input words to output symbols.