Bootstrapping techniques for polysynthetic morphological analysis

William Lane; Steven Bird

doi:10.18653/v1/2020.acl-main.594

Bootstrapping techniques for polysynthetic morphological analysis

William Lane, Steven Bird

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-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 language	English
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	https://doi.org/10.18653/v1/2020.acl-main.594
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
ISSN (Print)	0736-587X

Conference

Conference	58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
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

Access to Document

10.18653/v1/2020.acl-main.594

36427044-oaFinal published version, 275 KBLicence: CC BY

Cite this

Lane, W., & Bird, S. (2020). Bootstrapping techniques for polysynthetic morphological analysis. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Vol. 1, pp. 6652-6661). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.acl-main.594

Lane, William ; Bird, Steven. / Bootstrapping techniques for polysynthetic morphological analysis. ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. editor / Dan Jurafsky ; Joyce Chai ; Natalie Schluter ; Joel Tetreault. Vol. 1 Pennsylvania : Association for Computational Linguistics (ACL), 2020. pp. 6652-6661 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

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author = "William Lane and Steven Bird",

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: {\textcopyright} 2020 Association for Computational Linguistics; 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 ; Conference date: 05-07-2020 Through 10-07-2020",

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Lane, W & Bird, S 2020, Bootstrapping techniques for polysynthetic morphological analysis. in D Jurafsky, J Chai, N Schluter & J Tetreault (eds), ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. vol. 1, Proceedings of the Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), Pennsylvania, pp. 6652-6661, 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Virtual, Online, United States, 5/07/20. https://doi.org/10.18653/v1/2020.acl-main.594

Bootstrapping techniques for polysynthetic morphological analysis. / Lane, William; Bird, Steven.
ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. ed. / Dan Jurafsky; Joyce Chai; Natalie Schluter; Joel Tetreault. Vol. 1 Pennsylvania: Association for Computational Linguistics (ACL), 2020. p. 6652-6661 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

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N2 - 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.

AB - 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.

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Lane W, Bird S. Bootstrapping techniques for polysynthetic morphological analysis. In Jurafsky D, Chai J, Schluter N, Tetreault J, editors, ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Vol. 1. Pennsylvania: Association for Computational Linguistics (ACL). 2020. p. 6652-6661. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/2020.acl-main.594

Bootstrapping techniques for polysynthetic morphological analysis

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