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.
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Parsing Meaning Representations: is Easier Always Better?
The parsing accuracy varies a great deal for different meaning representations. In
this paper, we compare the parsing performances between Abstract Meaning Representation (AMR) and Minimal Recursion Semantics (MRS), and provide an in-depth analysis of what factors contributed to the discrepancy in their parsing accuracy. By crystalizing the trade-off between representation expressiveness and ease of automatic parsing, we
hope our results can help inform the design of
the next-generation meaning representations.
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- Award ID(s):
- 1763926
- PAR ID:
- 10109836
- Date Published:
- Journal Name:
- Proceedings of the First International Workshop on Designing Meaning Representations
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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