Conference article

Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered

Mika Hämäläinen

Niko Partanen

Jack Rueter

Khalid Alnajjar

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Published in: Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), May 31-June 2, 2021.

Linköping Electronic Conference Proceedings 178:17, p. 166-177

NEALT Proceedings Series 45:17, p. 166-177

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Published: 2021-05-21

ISBN: 978-91-7929-614-8

ISSN: 1650-3686 (print), 1650-3740 (online)


We train neural models for morphological analysis, generation and lemmatization for morphologically rich languages. We present a method for automatically extracting substantially large amount of training data from FSTs for 22 languages, out of which 17 are endangered. The neural models follow the same tagset as the FSTs in order to make it possible to use them as fallback systems together with the FSTs. The source code, models and datasets have been released on Zenodo.


morphology, endangered languages, resource building


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