Bootstrapping techniques for polysynthetic morphological analysis

William Lane, Steven Bird

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

7 Citations (Scopus)
56 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
EditorsDan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Place of PublicationPennsylvania
PublisherAssociation for Computational Linguistics (ACL)
Pages6652-6661
Number of pages15
Volume1
ISBN (Electronic)9781952148255
DOIs
Publication statusPublished - Jul 2020
Event58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
Duration: 5 Jul 202010 Jul 2020

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Country/TerritoryUnited States
CityVirtual, Online
Period5/07/2010/07/20

Bibliographical note

Funding Information:
We are grateful for the support of the Warddeken Rangers of West Arnhem. This work was covered by a research permit from the Northern Land Council, and was sponsored by the Australian government through a PhD scholarship, and grants from the Australian Research Council and the Indigenous Language and Arts Program. We are grateful to four anonymous reviewers for their feedback on an earlier version of this paper.

Publisher Copyright:
© 2020 Association for Computational Linguistics

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