Bootstrapping Techniques for Polysynthetic Morphological Analysis
W.A. Lane, Steven Bird · 2020
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 encoderdecoder 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.