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Modern French Poetry Generation with RoBERTa and GPT-2

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  • Additional Information
    • Contributors:
      Lattice - Langues, Textes, Traitements informatiques, Cognition - UMR 8094 (Lattice); Université Sorbonne Nouvelle - Paris 3-Université Sorbonne Paris Cité (USPC)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Sciences et Lettres (PSL)-Département Littératures et langage - ENS Paris (LILA); École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL); ICCC; ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)
    • Publication Information:
      HAL CCSD
    • Publication Date:
      2022
    • Collection:
      Université Sorbonne Nouvelle - Paris 3: HAL
    • Subject Terms:
    • Abstract:
      International audience ; We present a novel neural model for modern poetry generation in French. The model consists of two pretrained neural models that are fine-tuned for the poem generation task. The encoder of the model is a RoBERTa based one while the decoder is based on GPT-2. This way the model can benefit from the superior natural language understanding performance of RoBERTa and the good natural language generation performance of GPT-2. Our evaluation shows that the model can create French poetry successfully. On a 5 point scale, the lowest score of 3.57 was given by human judges to typicality and emotionality of the output poetry while the best score of 3.79 was given to understandability.
    • Relation:
      hal-03746349; https://cnrs.hal.science/hal-03746349; https://cnrs.hal.science/hal-03746349/document; https://cnrs.hal.science/hal-03746349/file/ICCC-2022_10S_Ha%CC%88ma%CC%88la%CC%88inen-et-al.pdf
    • Online Access:
      https://cnrs.hal.science/hal-03746349
      https://cnrs.hal.science/hal-03746349/document
      https://cnrs.hal.science/hal-03746349/file/ICCC-2022_10S_Ha%CC%88ma%CC%88la%CC%88inen-et-al.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.C4DDBF21