@inproceedings{post-etal-2024-accelerating,
    title = "Accelerating {UMR} Adoption: Neuro-Symbolic Conversion from {AMR}-to-{UMR} with Low Supervision",
    author = "Post, Claire Benet  and
      McGregor, Marie C.  and
      Pacheco, Maria Leonor  and
      Palmer, Alexis",
    editor = "Bonial, Claire  and
      Bonn, Julia  and
      Hwang, Jena D.",
    booktitle = "Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.dmr-1.15",
    pages = "140--150",
    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.",
}

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.
Post, Claire Benet McGregor, Marie C. Pacheco, Maria Leonor Palmer, Alexis
Accelerating UMR Adoption: Neuro-Symbolic Conversion from AMR-to-UMR with Low Supervision