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        <title>Paradigm Completion for Derivational Morphology</title>
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        <namePart type="given">Ryan</namePart>
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    <name type="personal">
        <namePart type="given">Ekaterina</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Christo</namePart>
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    <name type="personal">
        <namePart type="given">David</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models adapted from the inflection task are able to learn the range of derivation patterns, and outperform a non-neural baseline by 16.4%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.</abstract>
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%0 Conference Proceedings
%T Paradigm Completion for Derivational Morphology
%A Cotterell, Ryan
%A Vylomova, Ekaterina
%A Khayrallah, Huda
%A Kirov, Christo
%A Yarowsky, David
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F cotterell-etal-2017-paradigm
%X The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models adapted from the inflection task are able to learn the range of derivation patterns, and outperform a non-neural baseline by 16.4%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.
%R 10.18653/v1/D17-1074
%U https://aclanthology.org/D17-1074
%U https://doi.org/10.18653/v1/D17-1074
%P 714-720

Markdown (Informal)

[Paradigm Completion for Derivational Morphology](https://aclanthology.org/D17-1074) (Cotterell et al., EMNLP 2017)

• Paradigm Completion for Derivational Morphology (Cotterell et al., EMNLP 2017)

ACL

• Ryan Cotterell, Ekaterina Vylomova, Huda Khayrallah, Christo Kirov, and David Yarowsky. 2017. Paradigm Completion for Derivational Morphology. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 714–720, Copenhagen, Denmark. Association for Computational Linguistics.