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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Language-Model-Based Parsing and English Generation for Unscoped Episodic Logical Forms
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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- Award ID(s):
- 1940981
- NSF-PAR ID:
- 10359412
- Date Published:
- Journal Name:
- The International FLAIRS Conference Proceedings
- Volume:
- 35
- ISSN:
- 2334-0762
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.32473/flairs.v35i.130703
