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

• Main Authors: Hämäläinen, Mika, Partanen, Niko, Rueter, Jack, Alnajjar, Khalid
• Format: Journal Article
• Language: English
• Published: 26-05-2021
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.2105.12428

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.