Learning General Event Schemas with Episodic Logic

Lane Lawley, Benjamin Kuehnert, Lenhart Schubert


Abstract
We present a system for learning generalized, stereotypical patterns of events—or “schemas”—from natural language stories, and applying them to make predictions about other stories. Our schemas are represented with Episodic Logic, a logical form that closely mirrors natural language. By beginning with a “head start” set of protoschemas— schemas that a 1- or 2-year-old child would likely know—we can obtain useful, general world knowledge with very few story examples—often only one or two. Learned schemas can be combined into more complex, composite schemas, and used to make predictions in other stories where only partial information is available.
Anthology ID:
2021.naloma-1.1
Volume:
Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA)
Month:
June
Year:
2021
Address:
Groningen, the Netherlands (online)
Venues:
IWCS | NALOMA
SIG:
SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–6
Language:
URL:
https://aclanthology.org/2021.naloma-1.1
DOI:
Bibkey:
Cite (ACL):
Lane Lawley, Benjamin Kuehnert, and Lenhart Schubert. 2021. Learning General Event Schemas with Episodic Logic. In Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA), pages 1–6, Groningen, the Netherlands (online). Association for Computational Linguistics.
Cite (Informal):
Learning General Event Schemas with Episodic Logic (Lawley et al., NALOMA 2021)
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PDF:
https://aclanthology.org/2021.naloma-1.1.pdf