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        <title>SIGMORPHON 2020 Task 0 System Description: ETH Zürich Team</title>
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    <name type="personal">
        <namePart type="given">Martina</namePart>
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            <title>Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology</title>
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        <name type="personal">
            <namePart type="given">Garrett</namePart>
            <namePart type="family">Nicolai</namePart>
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            <namePart type="given">Kyle</namePart>
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        <name type="personal">
            <namePart type="given">Ryan</namePart>
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    <abstract>This paper presents our system for the SIGMORPHON 2020 Shared Task. We build off of the baseline systems, performing exact inference on models trained on language family data. Our systems return the globally best solution under these models. Our two systems achieve 80.9% and 75.6% accuracy on the test set. We ultimately find that, in this setting, exact inference does not seem to help or hinder the performance of morphological inflection generators, which stands in contrast to its affect on Neural Machine Translation (NMT) models.</abstract>
    <identifier type="citekey">forster-meister-2020-sigmorphon</identifier>
    <identifier type="doi">10.18653/v1/2020.sigmorphon-1.10</identifier>
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%0 Conference Proceedings
%T SIGMORPHON 2020 Task 0 System Description: ETH Zürich Team
%A Forster, Martina
%A Meister, Clara
%Y Nicolai, Garrett
%Y Gorman, Kyle
%Y Cotterell, Ryan
%S Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F forster-meister-2020-sigmorphon
%X This paper presents our system for the SIGMORPHON 2020 Shared Task. We build off of the baseline systems, performing exact inference on models trained on language family data. Our systems return the globally best solution under these models. Our two systems achieve 80.9% and 75.6% accuracy on the test set. We ultimately find that, in this setting, exact inference does not seem to help or hinder the performance of morphological inflection generators, which stands in contrast to its affect on Neural Machine Translation (NMT) models.
%R 10.18653/v1/2020.sigmorphon-1.10
%U https://aclanthology.org/2020.sigmorphon-1.10
%U https://doi.org/10.18653/v1/2020.sigmorphon-1.10
%P 106-110

Markdown (Informal)

[SIGMORPHON 2020 Task 0 System Description: ETH Zürich Team](https://aclanthology.org/2020.sigmorphon-1.10) (Forster & Meister, SIGMORPHON 2020)

• SIGMORPHON 2020 Task 0 System Description: ETH Zürich Team (Forster & Meister, SIGMORPHON 2020)

ACL

• Martina Forster and Clara Meister. 2020. SIGMORPHON 2020 Task 0 System Description: ETH Zürich Team. In Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 106–110, Online. Association for Computational Linguistics.