Accelerating UMR Adoption: Neuro-Symbolic Conversion from AMR-to-UMR with Low Supervision

Claire Benet Post, Marie C. McGregor, Maria Leonor Pacheco, Alexis Palmer

Abstract
Despite Uniform Meaning Representation’s (UMR) potential for cross-lingual semantics, limited annotated data has hindered its adoption. There are large datasets of English AMRs (Abstract Meaning Representations), but the process of converting AMR graphs to UMR graphs is non-trivial. In this paper we address a complex piece of that conversion process, namely cases where one AMR role can be mapped to multiple UMR roles through a non-deterministic process. We propose a neuro-symbolic method for role conversion, integrating animacy parsing and logic rules to guide a neural network, and minimizing human intervention. On test data, the model achieves promising accuracy, highlighting its potential to accelerate AMR-to-UMR conversion. Future work includes expanding animacy parsing, incorporating human feedback, and applying the method to broader aspects of conversion. This research demonstrates the benefits of combining symbolic and neural approaches for complex semantic tasks.
Anthology ID:
2024.dmr-1.15
Volume:
Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Claire Bonial, Julia Bonn, Jena D. Hwang
Venues:
DMR | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
140–150
Language:
URL:
https://aclanthology.org/2024.dmr-1.15
DOI:
Bibkey:
Cite (ACL):
Claire Benet Post, Marie C. McGregor, Maria Leonor Pacheco, and Alexis Palmer. 2024. Accelerating UMR Adoption: Neuro-Symbolic Conversion from AMR-to-UMR with Low Supervision. In Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024, pages 140–150, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Accelerating UMR Adoption: Neuro-Symbolic Conversion from AMR-to-UMR with Low Supervision (Post et al., DMR-WS 2024)
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