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        <title>Ensembles of Neural Morphological Inflection Models</title>
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        <namePart type="given">Ilmari</namePart>
        <namePart type="family">Kylliäinen</namePart>
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        <namePart type="given">Miikka</namePart>
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            <title>Proceedings of the 22nd Nordic Conference on Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Mareike</namePart>
            <namePart type="family">Hartmann</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Barbara</namePart>
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            <publisher>Linköping University Electronic Press</publisher>
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    <abstract>We investigate different ensemble learning techniques for neural morphological inflection using bidirectional LSTM encoder-decoder models with attention. We experiment with weighted and unweighted majority voting and bagging. We find that all investigated ensemble methods lead to improved accuracy over a baseline of a single model. However, contrary to expectation based on earlier work by Najafi et al. (2018) and Silfverberg et al. (2017), weighting does not deliver clear benefits. Bagging was found to underperform plain voting ensembles in general.</abstract>
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        <url>https://aclanthology.org/W19-6132</url>
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        <date>2019-sep–oct</date>
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%0 Conference Proceedings
%T Ensembles of Neural Morphological Inflection Models
%A Kylliäinen, Ilmari
%A Silfverberg, Miikka
%Y Hartmann, Mareike
%Y Plank, Barbara
%S Proceedings of the 22nd Nordic Conference on Computational Linguistics
%D 2019
%8 sep–oct
%I Linköping University Electronic Press
%C Turku, Finland
%F kylliainen-silfverberg-2019-ensembles
%X We investigate different ensemble learning techniques for neural morphological inflection using bidirectional LSTM encoder-decoder models with attention. We experiment with weighted and unweighted majority voting and bagging. We find that all investigated ensemble methods lead to improved accuracy over a baseline of a single model. However, contrary to expectation based on earlier work by Najafi et al. (2018) and Silfverberg et al. (2017), weighting does not deliver clear benefits. Bagging was found to underperform plain voting ensembles in general.
%U https://aclanthology.org/W19-6132
%P 304-309

Markdown (Informal)

[Ensembles of Neural Morphological Inflection Models](https://aclanthology.org/W19-6132) (Kylliäinen & Silfverberg, NoDaLiDa 2019)

• Ensembles of Neural Morphological Inflection Models (Kylliäinen & Silfverberg, NoDaLiDa 2019)

ACL

• Ilmari Kylliäinen and Miikka Silfverberg. 2019. Ensembles of Neural Morphological Inflection Models. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 304–309, Turku, Finland. Linköping University Electronic Press.