Monotonic Inference with Unscoped Episodic Logical Forms: From Principles to System

We describe the foundations and the systematization of natural logic-like monotonic inference using unscoped episodic logical forms (ULFs) that as reported by Kim et al. (Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA), Groningen, 2021a, b) introduced and first evaluated. In addition to providing a more detailed explanation of the theory and system, we present results from extending the inference manager to address a few of the limitations that as reported by Kim et al. (Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA), Groningen, 2021b) naive system has. Namely, we add mechanisms to incorporate lexical information from the hypothesis (or goal) sentence, enable the inference manager to consider multiple possible scopings for a single sentence, and match against the goal using English rather than the ULF.

Automated summary

This paper describes the foundation and systematization of natural logic-like monotonic inference using unscoped episodic logical forms (ULFs). It extends the inference manager to address limitations of the naive system by incorporating lexical information, considering multiple scopings, and matching against the goal using English.