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1. Language-Model-Based Parsing and English Generation for Unscoped Episodic Logical Forms

   https://doi.org/10.32473/flairs.v35i.130703  

   Gibson, Erin; Lawley, Lane (May 2022, The International FLAIRS Conference Proceedings)

   Unscoped Episodic Logical Forms (ULF) is a semantic representation for English sentences which captures semantic type structure, allows for linguistic inferences, and provides a basis for further resolution into Episodic Logic (EL). We present an application of pre-trained autoregressive language models to the task of rendering ULFs into English, and show that ULF's properties reduce the required training data volume for this approach when compared to AMR. We also show that the same system, when applied in reverse, performs well as an English-to-ULF parser. 

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2. Semantically informed data augmentation for unscoped episodic logical forms

   Juvekar, Mandar; Kim, Gene; Schubert, Lenhart (June 2023, 15th International Conference on Computational Semantics)

   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. 

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3. A (mostly) symbolic system for monotonic inference with unscoped Episodic Logical Forms

   Gene Louis Kim, Mandar Juvekar (June 2021, NAtural LOgic Meets MAchine Learning (NALOMA'21))

   null (Ed.)

   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. 

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4. Monotonic inference for underspecified episodic logic

   Kim, Gene Louis; Juvekar, Mandar; Schubert, Lenhart (July 2020, NaLoMa 2020: Natural Logic meets Machine Learning, Workshop at NASSLLI 2020, Brandeis University, Waltham MA,)

   null (Ed.)

   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. 

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5. Unrealizability Logic

   https://doi.org/10.1145/3571216  

   Kim, Jinwoo; D'Antoni, Loris; Reps, Thomas (January 2023, Proceedings of the ACM on Programming Languages)

   We consider the problem of establishing that a program-synthesis problem is unrealizable (i.e., has no solution in a given search space of programs). Prior work on unrealizability has developed some automatic techniques to establish that a problem is unrealizable; however, these techniques are all black-box , meaning that they conceal the reasoning behind why a synthesis problem is unrealizable. In this paper, we present a Hoare-style reasoning system, called unrealizability logic for establishing that a program-synthesis problem is unrealizable. To the best of our knowledge, unrealizability logic is the first proof system for overapproximating the execution of an infinite set of imperative programs. The logic provides a general, logical system for building checkable proofs about unrealizability. Similar to how Hoare logic distills the fundamental concepts behind algorithms and tools to prove the correctness of programs, unrealizability logic distills into a single logical system the fundamental concepts that were hidden within prior tools capable of establishing that a program-synthesis problem is unrealizable. 

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