Year: 2026
Type: conference-paper
Abstract: The paper presents the first shared task on parsing Uniform Meaning Representation (UMR), a graph-based framework for cross-linguistic semantic annotation of typologically diverse languages.The task requires systems to enrich plain text with sentence-level structure, node-token alignment, and document-level relations.It involves processing data for seven languages from four language families (Indo-European, Sino-Tibetan, Na-Dene, and Algic).Six languages have at least some training data; for one language, data is not available, leading to a zero-shot scenario.The training dataset as well as the gold-standard test set for all seven languages is released and made available for follow-up research.We present the task setup and evaluation methodology, using two graph matching approaches -a traditional, and an alignment-sensitive one, tailored specifically for UMR.Two participating systems are compared, each representing different modeling approaches.Results highlight the challenges of UMR parsing, particularly for alignment prediction and document-level semantics, and reveal substantial variation across languages and annotation conditions.
Institutions: Charles University, Brandeis University
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166513043
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