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Enhancing the Learning of Chinese Poem Creation Through an Auto-Generation and Evaluation System

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Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration (ICADL 2023)

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Abstract

Creating and evaluating Chinese poems is challenging for learners. To address this, we propose an auto-generation model and evaluation system to assist learners in poem creation and provide feedback on constraint adherence. We conducted a user experiment with 16 participants and generated up to 100 poems per keyword using 29 random keywords. The proposed system significantly improved poem completeness and usability, but did not enhance creation satisfaction. Users found it easy to confirm adherence to poem rules, but that verifying rhymes presented difficulties. The participants expressed overall satisfaction with the process, and the poems created were perceived as fluent and thematically consistent. However, limitations in keyword usage hinder emotional expression. This study highlights the need to overcome these challenges in order to facilitate learners’ creation and evaluation of Chinese poems.

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Acknowledgment

This work was supported by JSPS KAKENHI, Grant Numbers JP20K12543 and JP23K11761.

Author information

Authors and Affiliations

  1. Graduate School of Library, Information and Media Studies, University of Tsukuba, Tsukuba, Japan

    Yan Cong

  2. Institute of Library, Information and Media Science, University of Tsukuba, Tsukuba, Japan

    Masao Takaku

Authors
  1. Yan Cong
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  2. Masao Takaku
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Corresponding author

Correspondence to Yan Cong .

Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Dion H. Goh

  2. Academia Sinica, Taipei, Taiwan

    Shu-Jiun Chen

  3. Mahidol University, Tambon Salaya, Amphoe Phutthamonthon, Thailand

    Suppawong Tuarob

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cong, Y., Takaku, M. (2023). Enhancing the Learning of Chinese Poem Creation Through an Auto-Generation and Evaluation System. In: Goh, D.H., Chen, SJ., Tuarob, S. (eds) Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration. ICADL 2023. Lecture Notes in Computer Science, vol 14457. Springer, Singapore. https://doi.org/10.1007/978-981-99-8085-7_1

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  • DOI: https://doi.org/10.1007/978-981-99-8085-7_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8084-0

  • Online ISBN: 978-981-99-8085-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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