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        <title>A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer</title>
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    <name type="personal">
        <namePart type="given">Sarah</namePart>
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        <namePart type="given">Ghazaleh</namePart>
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    <name type="personal">
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            <title>Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages</title>
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            <namePart type="given">Judith</namePart>
            <namePart type="given">L</namePart>
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    <abstract>We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68% (unambiguous verbs) and 92.90% (all verbs).</abstract>
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%0 Conference Proceedings
%T A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer
%A Moeller, Sarah
%A Kazeminejad, Ghazaleh
%A Cowell, Andrew
%A Hulden, Mans
%Y Klavans, Judith L.
%S Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F moeller-etal-2018-neural
%X We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68% (unambiguous verbs) and 92.90% (all verbs).
%U https://aclanthology.org/W18-4802/
%P 12-20

Markdown (Informal)

[A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer](https://aclanthology.org/W18-4802/) (Moeller et al., PYLO 2018)

• A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer (Moeller et al., PYLO 2018)

ACL

• Sarah Moeller, Ghazaleh Kazeminejad, Andrew Cowell, and Mans Hulden. 2018. A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer. In Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages, pages 12–20, Santa Fe, New Mexico, USA. Association for Computational Linguistics.