Proceedings of the Annual Meeting of the Cognitive Science Society

Humans carry stereotypic tacit assumptions (STAs) (Prince,1978), or propositional beliefs about generic concepts. Suchassociations are crucial for understanding natural language.We construct a diagnostic set of word prediction prompts toevaluate whether recent neural contextualized language mod-els trained on large text corpora capture STAs. Our promptsare based on human responses in a psychological study of con-ceptual associations. We find models to be profoundly effec-tive at retrieving concepts given associated properties. Our re-sults demonstrate empirical evidence that stereotypic concep-tual representations are captured in neural models derived fromsemi-supervised linguistic exposure.