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1. Mapping AMR to UMR: Resources for Adapting Existing Corpora for Cross-Lingual Compatibility

   Bonn, Julia; Myers, Skatje; Van Gysel, Jens E.; Denk, Lukas; Vigus, Meagan; Zhao, Jin; Cowell, Andrew; Croft, William; Hajic, Jan; Martin, James H.; et al (March 2023, Proceedings of the 21st International Workshop on Treebanks and Linguistic Theories (TLT, GURT/SyntaxFest 2023))

   This paper presents detailed mappings between the structures used in Abstract Meaning Representation (AMR) and those used in Uniform Meaning Representation (UMR). These structures include general semantic roles, rolesets, and concepts that are largely shared between AMR and UMR, but with crucial differences. While UMR annotation of new low-resource languages is ongoing, AMR-annotated corpora already exist for many languages, and these AMR corpora are ripe for conversion to UMR format. Rather than focusing on semantic coverage that is new to UMR (which will likely need to be dealt with manually), this paper serves as a resource (with illustrated mappings) for users looking to understand the fine-grained adjustments that have been made to the representation techniques for semantic categories present in both AMR and UMR. 

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2. Bootstrapping UMR Annotations for Arapaho from Language Documentation Resources

   Buchholz, Matthew J; Bonn, Julia; Post, Claire Benet; Cowell, Andrew; Palmer, Alexis (May 2024, ELRA and ICCL)

   Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani; Xue, Nianwen (Ed.)

   Uniform Meaning Representation (UMR) is a semantic labeling system in the AMR family designed to be uniformly applicable to typologically diverse languages. The UMR labeling system is quite thorough and can be time-consuming to execute, especially if annotators are starting from scratch. In this paper, we focus on methods for bootstrapping UMR annotations for a given language from existing resources, and specifically from typical products of language documentation work, such as lexical databases and interlinear glossed text (IGT). Using Arapaho as our test case, we present and evaluate a bootstrapping process that automatically generates UMR subgraphs from IGT. Additionally, we describe and evaluate a method for bootstrapping valency lexicon entries from lexical databases for both the target language and English. We are able to generate enough basic structure in UMR graphs from the existing Arapaho interlinearized texts to automate UMR labeling to a significant extent. Our method thus has the potential to streamline the process of building meaning representations for new languages without existing large-scale computational resources. 

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3. Comparing UMR and Cross-lingual Adaptations of AMR

   Wein, Shira; Bonn, Julia (June 2023, Proceedings of the 4th International Workshop on Designing Meaning Representations)

   Abstract Meaning Representation (AMR) is a popular semantic annotation schema that presents sentence meaning as a graph while abstracting away from syntax. It was originally designed for English, but has since been extended to a variety of non-English versions. These cross-lingual adaptations, to varying degrees, incorporate language-specific features necessary to effectively capture the semantics of the language being annotated. Uniform Meaning Representation (UMR) on the other hand, the multilingual extension of AMR, was designed specifically for uniform cross-lingual application. In this work, we discuss these two approaches to extending AMR beyond English. We describe both approaches, compare the information they capture for a case language (Spanish), and outline implications for future work. 

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4. Modeling Quantification and Scope in Abstract Meaning Representations

   Pustejovksy, James; Xue, Nianwen; Lai, Kenneth (August 2019, Proceedings of the First International Workshop on Designing Meaning Representations)

   In this paper, we propose an extension to Abstract Meaning Representations (AMRs) to encode scope information of quantifiers and negation, in a way that overcomes the semantic gaps of the schema while maintaining its cognitive simplicity. Specifically, we address three phenomena not previously part of the AMR specification: quantification, negation (generally), and modality. The resulting representation, which we call “Uniform Meaning Representation” (UMR), adopts the predicative core of AMR and embeds it under a “scope” graph when appropriate. UMR representations differ from other treatments of quantification and modal scope phenomena in two ways: (a) they are more transparent; and (b) they specify default scope when possible. 

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5. Bootstrapping UMRs from Universal Dependencies for Scalable Multilingual Annotation

   Gamba, Federica; Palmer, Alexis; Zeman, Daniel (July 2025, Proceedings of the 19th Linguistic Annotation Workshop, Association for Computational Linguistics)

   Uniform Meaning Representation (UMR) is a semantic annotation framework designed to be applicable across typologically diverse languages. However, UMR annotation is a labor-intensive task, requiring significant effort and time especially when no prior annotations are available. In this paper, we present a method for bootstrapping UMR graphs by leveraging Universal Dependencies (UD), one of the most comprehensive multilingual resources, encompassing languages across a wide range of language families. Given UMR’s strong typological and cross-linguistic orientation, UD serves as a particularly suitable starting point for the conversion. We describe and evaluate an approach that automatically derives partial UMR graphs from UD trees, providing annotators with an initial representation to build upon. While UD is not a semantic resource, our method extracts useful structural information that aligns with the UMR formalism, thereby facilitating the annotation process. By leveraging UD’s broad typological coverage, this approach offers a scalable way to support UMR annotation across different languages. 

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