July 18, 2017
Conference Paper

Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks


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, displaying a degree of overlap similar to actual children. 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.

Revised: February 10, 2021 | Published: July 18, 2017


Phillips L.A., and N.O. Hodas. 2017. "Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks." In Proceedings of the 39th Annual Meeting of the Cognitive Sciencey Society (CogSci 2017), July 26-29, 2017, London, United Kingdom, 937-942. Austin, Texas:Cognitive Science Society. PNNL-SA-126047.

Research topics