SETUP: Sentence-level English-To-Uniform Meaning Representation Parser

• URL: http://arxiv.org/abs/2512.07068v1
• Date: Mon, 08 Dec 2025 00:56:00 GMT
• Title: SETUP: Sentence-level English-To-Uniform Meaning Representation Parser
• Abstract summary: Uniform Meaning Representation (UMR) is a graph-based semantic representation.<n>This paper introduces two methods for English text-to-UMR parsing.<n>Our best-performing model, which we call SETUP, achieves an AnCast score of 84 and a SMATCH++ score of 91.
• Score: 2.9494468099506896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uniform Meaning Representation (UMR) is a novel graph-based semantic representation which captures the core meaning of a text, with flexibility incorporated into the annotation schema such that the breadth of the world's languages can be annotated (including low-resource languages). While UMR shows promise in enabling language documentation, improving low-resource language technologies, and adding interpretability, the downstream applications of UMR can only be fully explored when text-to-UMR parsers enable the automatic large-scale production of accurate UMR graphs at test time. Prior work on text-to-UMR parsing is limited to date. In this paper, we introduce two methods for English text-to-UMR parsing, one of which fine-tunes existing parsers for Abstract Meaning Representation and the other, which leverages a converter from Universal Dependencies, using prior work as a baseline. Our best-performing model, which we call SETUP, achieves an AnCast score of 84 and a SMATCH++ score of 91, indicating substantial gains towards automatic UMR parsing.

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