Bootstrapping Techniques for Polysynthetic Morphological Analysis
Abstract
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.
Cite
@article{arxiv.2005.00956,
title = {Bootstrapping Techniques for Polysynthetic Morphological Analysis},
author = {William Lane and Steven Bird},
journal= {arXiv preprint arXiv:2005.00956},
year = {2020}
}