  Increasingly, cognitive scientists have demonstrated interest in applying
tools from deep learning. One use for deep learning is in language acquisition
where it is useful to know if a linguistic phenomenon can be learned through
domain-general means. To assess whether unsupervised deep learning is
appropriate, we first pose a smaller question: Can unsupervised neural networks
apply linguistic rules productively, using them in novel situations? We draw
from the literature on determiner/noun productivity by training an
unsupervised, autoencoder network measuring its ability to combine nouns with
determiners. Our simple autoencoder creates combinations it has not previously
encountered and produces a degree of overlap matching adults. While this
preliminary work does not provide conclusive evidence for productivity, it
warrants further investigation with more complex models. Further, this work
helps lay the foundations for future collaboration between the deep learning
and cognitive science communities.
