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Cited by 17Natural Language Processing TechniquesSyntax, Semantics, Linguistic VariationTopic Modeling

Morphological processing of low-resource languages: Where we are and what's next

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Wiemerslage, Adam, Silfverberg, Miikka, Yang, Changbing, McCarthy, Arya, Nicolai, Garrett, Colunga, Eliana, Kann, Katharina·2022·Findings of the Association for Computational Linguistics: ACL 2022
DOI 10.18653/v1/2022.findings-acl.80
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Abstract

Automatic morphological processing can aid downstream natural language processing applications, especially for low-resource languages, and assist language documentation efforts for endangered languages.Having long been multilingual, the field of computational morphology is increasingly moving towards approaches suitable for languages with minimal or no annotated resources.First, we survey recent developments in computational morphology with a focus on low-resource languages.Second, we argue that the field is ready to tackle the logical next challenge: understanding a language's morphology from raw text alone.We perform an empirical study on a truly unsupervised version of the paradigm completion task and show that, while existing state-of-the-art models bridged by two newly proposed models we devise perform reasonably, there is still much room for improvement.The stakes are high: solving this task will increase the language coverage of morphological resources by a number of magnitudes.

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  • Linguistic characteristics such as rich morphology, writing-system variation, and limited standardized tools further degrade retrieval performance for low-resource languages.

    “linguistic characteristics such as rich morphology, writing-system variation, and limited standardized tools further degrade retrieval performance (Wiemerslage et al., 2022).”

    From Improving Amharic Information Retrieval with Translative and Multi-Agent Debate Retrieval Augmented Generation (2026) · cites this paper as Wiemerslage et al., 2022

    The Introduction says morphological resources for low-resource languages are often small or nonexistent, and Section 4.2 notes orthographic diversity among the languages studied. But the paper does not show that rich morphology, writing-system variation, or a lack of standardized tools degrades information-retrieval performance; its reported performance concerns morphological processing. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Applying RAG to low-resource languages presents unique challenges, including poor embedding representations, low retrieval quality, and semantic gaps caused by scarcity of digital documents.

    “applying RAG to lowresource languages presents unique challenges, including poor embedding representations, low retrieval quality, and semantic gaps caused by the scarcity of digital documents.”

    From Improving Amharic Information Retrieval with Translative and Multi-Agent Debate Retrieval Augmented Generation (2026) · cites this paper as Wiemerslage et al., 2022

    The Abstract and Introduction discuss limited morphological resources for low-resource languages, not retrieval-augmented generation. Searches for RAG, embedding representations, retrieval quality, and scarcity of digital documents found no passage in our copy addressing the claimed RAG challenges. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cited by

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Related

Authors
Wiemerslage, A., Silfverberg, M., Yang, C., McCarthy, A., Nicolai, G., Colunga, E., & Kann, K.
Affiliations
University of British Columbia (CA)
ORCID
Venue
Findings of the Association for Computational Linguistics: ACL 2022
Publisher
Association for Computational Linguistics
Published
2022
Pages
988-1007
DOI
10.18653/v1/2022.findings-acl.80
OpenAlex
W4221155280
Cited by
16
References
2
Citations by year
2026: 4 · 2025: 4 · 2024: 1 · 2023: 7
FWCI
1.66
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
computational morphology, low-resource languages, endangered languages, language documentation, morphological resources, language coverage

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