CommonMorph: Participatory Morphological Documentation Platform

arXiv cs.CL / 4/7/2026

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Key Points

CommonMorph is introduced as an open-source platform to streamline the collection and annotation of morphological data, especially for low-resource languages and varieties where existing workflows are resource-intensive.
The system uses a three-tier process—expert linguistic definitions, contributor elicitation, and community validation—to reduce manual effort while maintaining methodological rigor.
It incorporates active learning and annotation suggestions, and provides tooling to import and adapt materials from related languages to accelerate development.
CommonMorph is designed to support multiple morphological typologies (fusional, agglutinative, and root-and-pattern) and can export UniMorph-compatible outputs for interoperability with NLP tools.
The platform is presented as a replicable collaborative technology approach, aimed at preserving linguistic diversity through accessible morphological documentation.

Abstract

Collecting and annotating morphological data present significant challenges, requiring linguistic expertise, methodological rigour, and substantial resources. These barriers are particularly acute for low-resource languages and varieties. To accelerate this process, we introduce \texttt{CommonMorph}, a comprehensive platform that streamlines morphological data collection development through a three-tiered approach: expert linguistic definition, contributor elicitation, and community validation. The platform minimises manual work by incorporating active learning, annotation suggestions, and tools to import and adapt materials from related languages. It accommodates diverse morphological systems, including fusional, agglutinative, and root-and-pattern morphologies. Its open-source design and UniMorph-compatible outputs ensure accessibility and interoperability with NLP tools. Our platform is accessible at https://common-morph.com, offering a replicable model for preserving linguistic diversity through collaborative technology.