Mining Logical Event Schemas From Pre-Trained Language Models

Lane Lawley; Lenhart Schubert

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arXiv · 2204.05939

Mining Logical Event Schemas From Pre-Trained Language Models

Lane Lawley·Lenhart Schubert

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Abstract

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.

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Lane Lawley, Lenhart Schubert. 2022-04-12. Mining Logical Event Schemas From Pre-Trained Language Models. https://arxiv.org/abs/2204.05939

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