  Deep neural networks drive the success of natural language processing. A
fundamental property of language is its compositional structure, allowing
humans to systematically produce forms for new meanings. For humans, languages
with more compositional and transparent structures are typically easier to
learn than those with opaque and irregular structures. However, this
learnability advantage has not yet been shown for deep neural networks,
limiting their use as models for human language learning. Here, we directly
test how neural networks compare to humans in learning and generalizing
different languages that vary in their degree of compositional structure. We
evaluate the memorization and generalization capabilities of a large language
model and recurrent neural networks, and show that both deep neural networks
exhibit a learnability advantage for more structured linguistic input: neural
networks exposed to more compositional languages show more systematic
generalization, greater agreement between different agents, and greater
similarity to human learners.
