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    <titleInfo>
        <title>Bootstrapping Techniques for Polysynthetic Morphological Analysis</title>
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    <name type="personal">
        <namePart type="given">William</namePart>
        <namePart type="family">Lane</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Joyce</namePart>
            <namePart type="family">Chai</namePart>
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        <name type="personal">
            <namePart type="given">Natalie</namePart>
            <namePart type="family">Schluter</namePart>
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            <namePart type="given">Joel</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.594</identifier>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T Bootstrapping Techniques for Polysynthetic Morphological Analysis
%A Lane, William
%A Bird, Steven
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F lane-bird-2020-bootstrapping
%X 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.
%R 10.18653/v1/2020.acl-main.594
%U https://aclanthology.org/2020.acl-main.594/
%U https://doi.org/10.18653/v1/2020.acl-main.594
%P 6652-6661

Markdown (Informal)

[Bootstrapping Techniques for Polysynthetic Morphological Analysis](https://aclanthology.org/2020.acl-main.594/) (Lane & Bird, ACL 2020)

• Bootstrapping Techniques for Polysynthetic Morphological Analysis (Lane & Bird, ACL 2020)

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.