  We present NESL (the Neuro-Episodic Schema Learner), an event schema learning
system that combines large language models, FrameNet parsing, a powerful
logical representation of language, and a set of simple behavioral schemas
meant to bootstrap the learning process. In lieu of a pre-made corpus of
stories, our dataset is a continuous feed of "situation samples" from a
pre-trained language model, which are then parsed into FrameNet frames, mapped
into simple behavioral schemas, and combined and generalized into complex,
hierarchical schemas for a variety of everyday scenarios. We show that careful
sampling from the language model can help emphasize stereotypical properties of
situations and de-emphasize irrelevant details, and that the resulting schemas
specify situations more comprehensively than those learned by other systems.
