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1. Building a Broad Infrastructure for Uniform Meaning Representations

   Bonn, Julia ; Buchholz, Matthew J ; Chun, Jayeol ; Cowell, Andrew ; Croft, William ; Denk, Lukas ; Ge, Sijia ; Hajič, Jan ; Lai, Kenneth ; Martin, James H ; et al ( May 2024 , ELRA and ICCL)

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

   This paper reports the first release of the UMR (Uniform Meaning Representation) data set. UMR is a graph-based meaning representation formalism consisting of a sentence-level graph and a document-level graph. The sentence-level graph represents predicate-argument structures, named entities, word senses, aspectuality of events, as well as person and number information for entities. The document-level graph represents coreferential, temporal, and modal relations that go beyond sentence boundaries. UMR is designed to capture the commonalities and variations across languages and this is done through the use of a common set of abstract concepts, relations, and attributes as well as concrete concepts derived from words from invidual languages. This UMR release includes annotations for six languages (Arapaho, Chinese, English, Kukama, Navajo, Sanapana) that vary greatly in terms of their linguistic properties and resource availability. We also describe on-going efforts to enlarge this data set and extend it to other genres and modalities. We also briefly describe the available infrastructure (UMR annotation guidelines and tools) that others can use to create similar data sets. 

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2. Designing a Uniform Meaning Representation for Natural Language Processing

   https://doi.org/10.1007/s13218-021-00722-w  

   Van Gysel, Jens E. ; Vigus, Meagan ; Chun, Jayeol ; Lai, Kenneth ; Moeller, Sarah ; Yao, Jiarui ; O’Gorman, Tim ; Cowell, Andrew ; Croft, William ; Huang, Chu-Ren ; et al ( April 2021 , KI - Künstliche Intelligenz)

   null (Ed.)

   In this paper we present Uniform Meaning Representation (UMR), a meaning representation designed to annotate the semantic content of a text. UMR is primarily based on Abstract Meaning Representation (AMR), an annotation framework initially designed for English, but also draws from other meaning representations. UMR extends AMR to other languages, particularly morphologically complex, low-resource languages. UMR also adds features to AMR that are critical to semantic interpretation and enhances AMR by proposing a companion document-level representation that captures linguistic phenomena such as coreference as well as temporal and modal dependencies that potentially go beyond sentence boundaries. 

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3. UMR Annotation of Multiword Expressions

   Bonn, Julia ; Cowell, Andrew ; Hajic, Jan ; Palmer, Alexis ; Palmer, Martha ; Pustejovsky, James ; Sun, Haibo ; Uresova, Zdenka ; Wein, Shira ; Xue, Nianwen ; et al ( June 2023 , Proceedings of the 4th International Workshop on Designing Meaning Representations)

   Rooted in AMR, Uniform Meaning Representation (UMR) is a graph-based formalism with nodes as concepts and edges as relations between them. When used to represent natural language semantics, UMR maps words in a sentence to concepts in the UMR graph. Multiword expressions (MWEs) pose a particular challenge to UMR annotation because they deviate from the default one-to-one mapping between words and concepts. There are different types of MWEs which require different kinds of annotation that must be specified in guidelines. This paper discusses the specific treatment for each type of MWE in UMR. 

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4. 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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5. 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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