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1. UMR-Writer 2.0: Incorporating a New Keyboard Interface and Workflow into UMR-Writer

   Ge, Sijia; Wright-Bettner, Kristin; Myers, Skatje; Xue, Nianwen; Palmer, Martha (July 2023, Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII))

   UMR-Writer is a web-based tool for annotating semantic graphs with the Uniform Meaning Representation (UMR) scheme. UMR is a graph-based semantic representation that can be applied cross-linguistically for deep semantic analysis of texts. In this work, we implemented a new keyboard interface in UMR-Writer 2.0, which is a powerful addition to the original mouse interface, supporting faster annotation for more experienced annotators. The new interface also addresses issues with the original mouse interface. Additionally, we demonstrate an efficient workflow for annotation project management in UMR-Writer 2.0, which has been applied to many projects. 

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

   Post, Claire Benet; McGregor, Marie C; Pacheco, Maria Leonor; Palmer, Alexis (May 2024, ELRA and ICCL)

   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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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, The 4th International Workshop on Designing Meaning Representations)
4. 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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5. Chinese UMR annotation: Can LLMs help?

   Sun, Haibo; Xue, Nianwen; Zhao, Jin; Yue, Liulu; Sun, Yao; Xue, Keer; Wu, Jiawei (May 2024, ELRA and ICCL)

   Bonial, Claire; Bonn, Julia; Hwang, Jena D (Ed.)

   We explore using LLMs, GPT-4 specifically, to generate draft sentence-level Chinese Uniform Meaning Representations (UMRs) that human annotators can revise to speed up the UMR annotation process. In this study, we use few-shot learning and Think-Aloud prompting to guide GPT-4 to generate sentence-level graphs of UMR. Our experimental results show that compared with annotating UMRs from scratch, using LLMs as a preprocessing step reduces the annotation time by two thirds on average. This indicates that there is great potential for integrating LLMs into the pipeline for complicated semantic annotation tasks. 

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