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)
- Editors:
- Simon Dobnik, Lilja Øvrelid
- Venue:
- NoDaLiDa
- SIG:
- Publisher:
- Linköping University Electronic Press, Sweden
- Note:
- Pages:
- 166–177
- Language:
- URL:
- https://aclanthology.org/2021.nodalida-main.17
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.nodalida-main.17.pdf
- Code
- mikahama/uralicNLP