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This content will become publicly available on July 20, 2026

Title: Bootstrapping UMRs from Universal Dependencies for Scalable Multilingual Annotation
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.  more » « less
Award ID(s):
2213805
PAR ID:
10599366
Author(s) / Creator(s):
; ;
Publisher / Repository:
Proceedings of the 19th Linguistic Annotation Workshop, Association for Computational Linguistics
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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