Bootstrapping techniques for polysynthetic morphological analysis

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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 publicationProceedings of the 58th Annual Meeting of the Association for Computational Linguistics
EditorsDan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Place of PublicationPennsylvania
PublisherAssociation for Computational Linguistics (ACL)
Number of pages15
ISBN (Electronic)978-1-952148-25-5
Publication statusPublished - Jul 2020
Event58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Online
Duration: 5 Jul 202010 Jul 2020


Conference58th Annual Meeting of the Association for Computational Linguistics, ACL 2020


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