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Creators/Authors contains: "Palmer, Martha"

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  1. ; ; ; ; ; ; ; ; ; ; et al ( , AI Magazine)
    Abstract

    The Institute for Student‐AI Teaming (iSAT) addresses the foundational question:how to promote deep conceptual learning via rich socio‐collaborative learning experiences for all students?—a question that is ripe for AI‐based facilitation and has the potential to transform classrooms. We advance research in speech, computer vision, human‐agent teaming, computer‐supported collaborative learning, expansive co‐design, and the science of broadening participation to design and study next generation AI technologies (called AI Partners) embedded in student collaborative learning teams in coordination with teachers. Our institute ascribes to theoretical perspectives that aim to create a normative environment of widespread engagement through responsible design of technology, curriculum, and pedagogy in partnership with K–12 educators, racially diverse students, parents, and other community members.

     
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    Free, publicly-accessible full text available March 1, 2025
  2. ; ; ; ; ( , 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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    Free, publicly-accessible full text available July 1, 2024
  3. ; ; ; ; ; ( , The 17th Linguistic Annotation Workshop (LAW-XVII))
    Free, publicly-accessible full text available July 13, 2024
  4. ; ; ; ; ; ; ; ; ; ; et al ( , The 4th International Workshop on Designing Meaning Representations)
    Free, publicly-accessible full text available June 20, 2024
  5. ; ; ; ; ; ; ; ; ; ( , Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization)
    Free, publicly-accessible full text available June 18, 2024
  6. ; ; ; ; ; ; ; ; ; ; et al ( , 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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    Free, publicly-accessible full text available June 1, 2024
  7. ; ; ( , Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023))
    Full Text Available 
  8. ; ; ; ; ; ( , Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)
    Full Text Available 
  9. ; ; ; ; ; ; ; ; ; ; et al ( , The 21st International Workshop on Treebanks and Linguistic Theories (TLT, GURT/SyntaxFest 2023))
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; ; ; ; ; ; et al ( , 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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    Accepted Manuscript Full Text Available