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        <title>Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?</title>
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        <namePart type="given">Alexey</namePart>
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            <namePart type="given">Garrett</namePart>
            <namePart type="family">Nicolai</namePart>
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        <name type="personal">
            <namePart type="given">Ryan</namePart>
            <namePart type="family">Cotterell</namePart>
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    <abstract>We apply convolutional neural networks to the task of shallow morpheme segmentation using low-resource datasets for 5 different languages. We show that both in fully supervised and semi-supervised settings our model beats previous state-of-the-art approaches. We argue that convolutional neural networks reflect local nature of morpheme segmentation better than other semi-supervised approaches.</abstract>
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%0 Conference Proceedings
%T Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?
%A Sorokin, Alexey
%Y Nicolai, Garrett
%Y Cotterell, Ryan
%S Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F sorokin-2019-convolutional
%X We apply convolutional neural networks to the task of shallow morpheme segmentation using low-resource datasets for 5 different languages. We show that both in fully supervised and semi-supervised settings our model beats previous state-of-the-art approaches. We argue that convolutional neural networks reflect local nature of morpheme segmentation better than other semi-supervised approaches.
%R 10.18653/v1/W19-4218
%U https://aclanthology.org/W19-4218/
%U https://doi.org/10.18653/v1/W19-4218
%P 154-159

Markdown (Informal)

[Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?](https://aclanthology.org/W19-4218/) (Sorokin, ACL 2019)

• Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art? (Sorokin, ACL 2019)

ACL

• Alexey Sorokin. 2019. Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?. In Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 154–159, Florence, Italy. Association for Computational Linguistics.