# Bootstrapping Techniques for Polysynthetic Morphological Analysis

arXiv: https://arxiv.org/abs/2005.00956 · HTML: https://mlsanity.com/paper/2005.00956/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2020.

## Built on by (most cited first, 4 of 4)

- [Not always about you: Prioritizing community needs when developing endangered language technology](https://mlsanity.com/paper/2204.05541/) (2022)
- [Understanding Compositional Data Augmentation in Typologically Diverse Morphological Inflection](https://mlsanity.com/paper/2305.13658/) (2023)
- [Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Underdocumented Languages](https://mlsanity.com/paper/2203.09632/) (2022)
- [Is linguistically-motivated data augmentation worth it?](https://mlsanity.com/paper/2506.03593/) (2025)

## Abstract (arXiv)

Polysynthetic languages have exceptionally large and sparse vocabularies, thanks to the number of morpheme slots and combinations in a word. This complexity, together with a general scarcity of written data, poses a challenge to the development of natural language technologies. To address this challenge, we offer linguistically-informed approaches for bootstrapping a neural morphological analyzer, and demonstrate its application to Kunwinjku, a polysynthetic Australian language. We generate data from a finite state transducer to train an encoder-decoder model. We improve the model by "hallucinating" missing linguistic structure into the training data, and by resampling from a Zipf distribution to simulate a more natural distribution of morphemes. The best model accounts for all instances of reduplication in the test set and achieves an accuracy of 94.7% overall, a 10 percentage point improvement over the FST baseline. This process demonstrates the feasibility of bootstrapping a neural morph analyzer from minimal resources.