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        <title>Introducing Aspects of Creativity in Automatic Poetry Generation</title>
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        <namePart type="given">Brendan</namePart>
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            <title>Proceedings of the 16th International Conference on Natural Language Processing</title>
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            <namePart type="given">Dipti</namePart>
            <namePart type="given">Misra</namePart>
            <namePart type="family">Sharma</namePart>
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            <namePart type="given">Pushpak</namePart>
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    <abstract>Poetry Generation involves teaching systems to automatically generate text that resembles poetic work. A deep learning system can learn to generate poetry on its own by training on a corpus of poems and modeling the particular style of language. In this paper, we propose taking an approach that fine-tunes GPT-2, a pre-trained language model, to our downstream task of poetry generation. We extend prior work on poetry generation by introducing creative elements. Specifically, we generate poems that express emotion and elicit the same in readers, and poems that use the language of dreams—called dream poetry. We are able to produce poems that correctly elicit the emotions of sadness and joy 87.5 and 85 percent, respectively, of the time. We produce dreamlike poetry by training on a corpus of texts that describe dreams. Poems from this model are shown to capture elements of dream poetry with scores of no less than 3.2 on the Likert scale. We perform crowdsourced human-evaluation for all our poems. We also make use of the Coh-Metrix tool, outlining metrics we use to gauge the quality of text generated.</abstract>
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%0 Conference Proceedings
%T Introducing Aspects of Creativity in Automatic Poetry Generation
%A Bena, Brendan
%A Kalita, Jugal
%Y Sharma, Dipti Misra
%Y Bhattacharya, Pushpak
%S Proceedings of the 16th International Conference on Natural Language Processing
%D 2019
%8 December
%I NLP Association of India
%C International Institute of Information Technology, Hyderabad, India
%F bena-kalita-2019-introducing
%X Poetry Generation involves teaching systems to automatically generate text that resembles poetic work. A deep learning system can learn to generate poetry on its own by training on a corpus of poems and modeling the particular style of language. In this paper, we propose taking an approach that fine-tunes GPT-2, a pre-trained language model, to our downstream task of poetry generation. We extend prior work on poetry generation by introducing creative elements. Specifically, we generate poems that express emotion and elicit the same in readers, and poems that use the language of dreams—called dream poetry. We are able to produce poems that correctly elicit the emotions of sadness and joy 87.5 and 85 percent, respectively, of the time. We produce dreamlike poetry by training on a corpus of texts that describe dreams. Poems from this model are shown to capture elements of dream poetry with scores of no less than 3.2 on the Likert scale. We perform crowdsourced human-evaluation for all our poems. We also make use of the Coh-Metrix tool, outlining metrics we use to gauge the quality of text generated.
%U https://aclanthology.org/2019.icon-1.4
%P 26-35

Markdown (Informal)

[Introducing Aspects of Creativity in Automatic Poetry Generation](https://aclanthology.org/2019.icon-1.4) (Bena & Kalita, ICON 2019)

• Introducing Aspects of Creativity in Automatic Poetry Generation (Bena & Kalita, ICON 2019)

ACL

• Brendan Bena and Jugal Kalita. 2019. Introducing Aspects of Creativity in Automatic Poetry Generation. In Proceedings of the 16th International Conference on Natural Language Processing, pages 26–35, International Institute of Information Technology, Hyderabad, India. NLP Association of India.