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        <title>Mining Logical Event Schemas From Pre-Trained Language Models</title>
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        <name type="personal">
            <namePart type="given">Samuel</namePart>
            <namePart type="family">Louvan</namePart>
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        <name type="personal">
            <namePart type="given">Andrea</namePart>
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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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.acl-srw.25</identifier>
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%0 Conference Proceedings
%T Mining Logical Event Schemas From Pre-Trained Language Models
%A Lawley, Lane
%A Schubert, Lenhart
%Y Louvan, Samuel
%Y Madotto, Andrea
%Y Madureira, Brielen
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F lawley-schubert-2022-mining
%X 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.
%R 10.18653/v1/2022.acl-srw.25
%U https://aclanthology.org/2022.acl-srw.25/
%U https://doi.org/10.18653/v1/2022.acl-srw.25
%P 332-345

Markdown (Informal)

[Mining Logical Event Schemas From Pre-Trained Language Models](https://aclanthology.org/2022.acl-srw.25/) (Lawley & Schubert, ACL 2022)

• Mining Logical Event Schemas From Pre-Trained Language Models (Lawley & Schubert, ACL 2022)

ACL

• Lane Lawley and Lenhart Schubert. 2022. Mining Logical Event Schemas From Pre-Trained Language Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 332–345, Dublin, Ireland. Association for Computational Linguistics.