Unscoped Logical Form (ULF) of Episodic Logic is a meaning representation format that captures the overall semantic type structure of natural language while leaving certain finer details, such as word sense and quantifier scope, underspecified for ease of parsing and annotation. While a learned parser exists to convert English to ULF, its performance is severely limited by the lack of a large dataset to train the system. We present a ULF dataset augmentation method that samples type-coherent ULF expressions using the ULF semantic type system and filters out samples corresponding to implausible English sentences using a pretrained language model. Our data augmentation method is configurable with parameters that trade off between plausibility of samples with sample novelty and augmentation size. We find that the best configuration of this augmentation method substantially improves parser performance beyond using the existing unaugmented dataset.

We present a method of making natural logic inferences from Unscoped Logical Form of Episodic Logic. We establish a correspondence between inference rules of scope resolved Episodic Logic and the natural logic treatment by Sánchez Valencia (1991a), and hence demonstrate the ability to handle foundational natural logic inferences from prior literature as well as more general nested monotonicity inferences.

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.