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.- Anthology ID:
- 2022.acl-srw.25
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Samuel Louvan, Andrea Madotto, Brielen Madureira
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 332–345
- Language:
- URL:
- https://aclanthology.org/2022.acl-srw.25
- DOI:
- 10.18653/v1/2022.acl-srw.25
- Cite (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.
- Cite (Informal):
- Mining Logical Event Schemas From Pre-Trained Language Models (Lawley & Schubert, ACL 2022)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2022.acl-srw.25.pdf
- Data
- FrameNet