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 language | English |
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Title of host publication | ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference |
Editors | Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault |
Place of Publication | Pennsylvania |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 6652-6661 |
Number of pages | 15 |
Volume | 1 |
ISBN (Electronic) | 9781952148255 |
DOIs | |
Publication status | Published - Jul 2020 |
Event | 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States Duration: 5 Jul 2020 → 10 Jul 2020 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
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ISSN (Print) | 0736-587X |
Conference
Conference | 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 5/07/20 → 10/07/20 |
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