  Exemplar based accounts are often considered to be in direct opposition to
pure linguistic abstraction in explaining language learners' ability to
generalize to novel expressions. However, the recent success of neural network
language models on linguistically sensitive tasks suggests that perhaps
abstractions can arise via the encoding of exemplars. We provide empirical
evidence for this claim by adapting an existing experiment that studies how an
LM (BERT) generalizes the usage of novel tokens that belong to lexical
categories such as Noun/Verb/Adjective/Adverb from exposure to only a single
instance of their usage. We analyze the representational behavior of the novel
tokens in these experiments, and find that BERT's capacity to generalize to
unseen expressions involving the use of these novel tokens constitutes the
movement of novel token representations towards regions of known category
exemplars in two-dimensional space. Our results suggest that learners' encoding
of exemplars can indeed give rise to abstraction like behavior.
