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        <title>Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting</title>
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        <namePart type="given">Peter</namePart>
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        <namePart type="given">Alexey</namePart>
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            <title>Proceedings of the Second Workshop on Stylistic Variation</title>
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            <namePart type="given">Julian</namePart>
            <namePart type="family">Brooke</namePart>
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        <name type="personal">
            <namePart type="given">Lucie</namePart>
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            <namePart type="given">Moshe</namePart>
            <namePart type="family">Koppel</namePart>
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    <abstract>Language generation tasks that seek to mimic human ability to use language creatively are difficult to evaluate, since one must consider creativity, style, and other non-trivial aspects of the generated text. The goal of this paper is to develop evaluations methods for one such task, ghostwriting of rap lyrics, and to provide an explicit, quantifiable foundation for the goals and future directions for this task. Ghostwriting must produce text that is similar in style to the emulated artist, yet distinct in content. We develop a novel evaluation methodology that addresses several complementary aspects of this task, and illustrate how such evaluation can be used to meaning fully analyze system performance. We provide a corpus of lyrics for 13 rap artists, annotated for stylistic similarity, which allows us to assess the feasibility of manual evaluation for generated verse.</abstract>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting
%A Potash, Peter
%A Romanov, Alexey
%A Rumshisky, Anna
%Y Brooke, Julian
%Y Flekova, Lucie
%Y Koppel, Moshe
%Y Solorio, Thamar
%S Proceedings of the Second Workshop on Stylistic Variation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans
%F potash-etal-2018-evaluating
%X Language generation tasks that seek to mimic human ability to use language creatively are difficult to evaluate, since one must consider creativity, style, and other non-trivial aspects of the generated text. The goal of this paper is to develop evaluations methods for one such task, ghostwriting of rap lyrics, and to provide an explicit, quantifiable foundation for the goals and future directions for this task. Ghostwriting must produce text that is similar in style to the emulated artist, yet distinct in content. We develop a novel evaluation methodology that addresses several complementary aspects of this task, and illustrate how such evaluation can be used to meaning fully analyze system performance. We provide a corpus of lyrics for 13 rap artists, annotated for stylistic similarity, which allows us to assess the feasibility of manual evaluation for generated verse.
%R 10.18653/v1/W18-1604
%U https://aclanthology.org/W18-1604
%U https://doi.org/10.18653/v1/W18-1604
%P 29-38

Markdown (Informal)

[Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting](https://aclanthology.org/W18-1604) (Potash et al., Style-Var 2018)

• Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting (Potash et al., Style-Var 2018)

ACL

• Peter Potash, Alexey Romanov, and Anna Rumshisky. 2018. Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting. In Proceedings of the Second Workshop on Stylistic Variation, pages 29–38, New Orleans. Association for Computational Linguistics.