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

Mika Hämäläinen, Niko Partanen, Jack Rueter, Khalid Alnajjar


Abstract
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.
Anthology ID:
2021.nodalida-main.17
Volume:
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)
Month:
May 31--2 June
Year:
2021
Address:
Reykjavik, Iceland (Online)
Venue:
NoDaLiDa
SIG:
Publisher:
Linköping University Electronic Press, Sweden
Note:
Pages:
166–177
Language:
URL:
https://aclanthology.org/2021.nodalida-main.17
DOI:
Bibkey:
Cite (ACL):
Mika Hämäläinen, Niko Partanen, Jack Rueter, and Khalid Alnajjar. 2021. Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), pages 166–177, Reykjavik, Iceland (Online). Linköping University Electronic Press, Sweden.
Cite (Informal):
Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered (Hämäläinen et al., NoDaLiDa 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nodalida-main.17.pdf
Code
 mikahama/uralicNLP