Bootstrapping Techniques for Polysynthetic Morphological Analysis

William Lane, Steven Bird


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.
Anthology ID:
2020.acl-main.594
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6652–6661
Language:
URL:
https://aclanthology.org/2020.acl-main.594
DOI:
10.18653/v1/2020.acl-main.594
Bibkey:
Cite (ACL):
William Lane and Steven Bird. 2020. Bootstrapping Techniques for Polysynthetic Morphological Analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6652–6661, Online. Association for Computational Linguistics.
Cite (Informal):
Bootstrapping Techniques for Polysynthetic Morphological Analysis (Lane & Bird, ACL 2020)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.acl-main.594.pdf
Video:
 http://slideslive.com/38928893