A (Mostly) Symbolic System for Monotonic Inference with Unscoped Episodic Logical Forms

Gene Kim, Mandar Juvekar, Junis Ekmekciu, Viet Duong, Lenhart Schubert


Abstract
We implement the formalization of natural logic-like monotonic inference using Unscoped Episodic Logical Forms (ULFs) by Kim et al. (2020). We demonstrate this system’s capacity to handle a variety of challenging semantic phenomena using the FraCaS dataset (Cooper et al., 1996). These results give empirical evidence for prior claims that ULF is an appropriate representation to mediate natural logic-like inferences.
Anthology ID:
2021.naloma-1.9
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:
71–80
Language:
URL:
https://aclanthology.org/2021.naloma-1.9
DOI:
Bibkey:
Cite (ACL):
Gene Kim, Mandar Juvekar, Junis Ekmekciu, Viet Duong, and Lenhart Schubert. 2021. A (Mostly) Symbolic System for Monotonic Inference with Unscoped Episodic Logical Forms. In Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA), pages 71–80, Groningen, the Netherlands (online). Association for Computational Linguistics.
Cite (Informal):
A (Mostly) Symbolic System for Monotonic Inference with Unscoped Episodic Logical Forms (Kim et al., NALOMA 2021)
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https://aclanthology.org/2021.naloma-1.9.pdf
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