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        <title>Supervised Rhyme Detection with Siamese Recurrent Networks</title>
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    <name type="personal">
        <namePart type="given">Thomas</namePart>
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    <name type="personal">
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            <title>Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature</title>
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        <name type="personal">
            <namePart type="given">Beatrice</namePart>
            <namePart type="family">Alex</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Stefania</namePart>
            <namePart type="family">Degaetano-Ortlieb</namePart>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Feldman</namePart>
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            <namePart type="given">Anna</namePart>
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            <namePart type="given">Nils</namePart>
            <namePart type="family">Reiter</namePart>
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        <name type="personal">
            <namePart type="given">Stan</namePart>
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    <abstract>We present the first supervised approach to rhyme detection with Siamese Recurrent Networks (SRN) that offer near perfect performance (97% accuracy) with a single model on rhyme pairs for German, English and French, allowing future large scale analyses. SRNs learn a similarity metric on variable length character sequences that can be used as judgement on the distance of imperfect rhyme pairs and for binary classification. For training, we construct a diachronically balanced rhyme goldstandard of New High German (NHG) poetry. For further testing, we sample a second collection of NHG poetry and set of contemporary Hip-Hop lyrics, annotated for rhyme and assonance. We train several high-performing SRN models and evaluate them qualitatively on selected sonnetts.</abstract>
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        <date>2018-08</date>
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            <start>81</start>
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%0 Conference Proceedings
%T Supervised Rhyme Detection with Siamese Recurrent Networks
%A Haider, Thomas
%A Kuhn, Jonas
%Y Alex, Beatrice
%Y Degaetano-Ortlieb, Stefania
%Y Feldman, Anna
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F haider-kuhn-2018-supervised
%X We present the first supervised approach to rhyme detection with Siamese Recurrent Networks (SRN) that offer near perfect performance (97% accuracy) with a single model on rhyme pairs for German, English and French, allowing future large scale analyses. SRNs learn a similarity metric on variable length character sequences that can be used as judgement on the distance of imperfect rhyme pairs and for binary classification. For training, we construct a diachronically balanced rhyme goldstandard of New High German (NHG) poetry. For further testing, we sample a second collection of NHG poetry and set of contemporary Hip-Hop lyrics, annotated for rhyme and assonance. We train several high-performing SRN models and evaluate them qualitatively on selected sonnetts.
%U https://aclanthology.org/W18-4509
%P 81-86

Markdown (Informal)

[Supervised Rhyme Detection with Siamese Recurrent Networks](https://aclanthology.org/W18-4509) (Haider & Kuhn, LaTeCH 2018)

• Supervised Rhyme Detection with Siamese Recurrent Networks (Haider & Kuhn, LaTeCH 2018)

ACL

• Thomas Haider and Jonas Kuhn. 2018. Supervised Rhyme Detection with Siamese Recurrent Networks. In Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 81–86, Santa Fe, New Mexico. Association for Computational Linguistics.