Morphological Processing of Low-Resource Languages: Where We Are and What’s Next

Adam Wiemerslage, Miikka Silfverberg, Changbing Yang, Arya McCarthy, Garrett Nicolai, Eliana Colunga, Katharina Kann


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.
Anthology ID:
2022.findings-acl.80
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
988–1007
Language:
URL:
https://aclanthology.org/2022.findings-acl.80
DOI:
10.18653/v1/2022.findings-acl.80
Bibkey:
Cite (ACL):
Adam Wiemerslage, Miikka Silfverberg, Changbing Yang, Arya McCarthy, Garrett Nicolai, Eliana Colunga, and Katharina Kann. 2022. Morphological Processing of Low-Resource Languages: Where We Are and What’s Next. In Findings of the Association for Computational Linguistics: ACL 2022, pages 988–1007, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Morphological Processing of Low-Resource Languages: Where We Are and What’s Next (Wiemerslage et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-acl.80.pdf