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

arXiv: https://arxiv.org/abs/2105.12428 · HTML: https://mlsanity.com/paper/2105.12428/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 9 of 9)

- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [UniMorph 2.0: Universal Morphology](https://mlsanity.com/paper/1810.11101/) (2018)
- [Endangered Languages are not Low-Resourced!](https://mlsanity.com/paper/2103.09567/) (2021)
- [Improving Lemmatization of Non-Standard Languages with Joint Learning](https://mlsanity.com/paper/1903.06939/) (2019)
- [Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging](https://mlsanity.com/paper/1910.02267/) (2019)
- [Universal Word Segmentation: Implementation and Interpretation](https://mlsanity.com/paper/1807.02974/) (2018)
- [A Simple Joint Model for Improved Contextual Neural Lemmatization](https://mlsanity.com/paper/1904.02306/) (2019)
- [Neural disambiguation of lemma and part of speech in morphologically rich languages](https://mlsanity.com/paper/2007.06104/) (2020)

## Built on by (most cited first, 5 of 5)

- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)
- [Evaluating OpenAI GPT Models for Translation of Endangered Uralic Languages: A Comparison of Reasoning and Non-Reasoning Architectures](https://mlsanity.com/paper/2512.16287/) (2025)
- [SampoNLP: A Self-Referential Toolkit for Morphological Analysis of Subword Tokenizers](https://mlsanity.com/paper/2601.04469/) (2026)
- [Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages](https://mlsanity.com/paper/2305.15380/) (2023)
- [Leveraging Transformer-Based Models for Predicting Inflection Classes of Words in an Endangered Sami Language](https://mlsanity.com/paper/2411.02556/) (2024)

## Abstract (arXiv)

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.