Rhyming Knowledge-Aware Deep Neural Network for Chinese Poetry Generation

Wen Chao Yeh, Yung Chun Chang, Yu Hsuan Li, Wei Chieh Chang

研究成果: 書貢獻/報告類型會議貢獻

摘要

Analyzing and capturing the spirit in the historic Tang Dynasty poems for creating a machine that can compose new poetry is a difficult but fun challenge. In this research, we propose a rhyming knowledge-aware deep neural network for Chinese poetry generation. The model fuses rhyming knowledge that represents phonological tones into a long short-term memory (LSTM) model. This work will help us understand more about what kind of mechanism within the neural network contributes to different styles of the generated poems. The experimental results demonstrate that the proposed method is able to guide the style of those poems towards higher phonological compliance, fluency, coherence, and meaningfulness, as evaluated by human experts. We believe that future research can adopt our approach to further integrate more knowledge such as sentiments, POS, and even stylistic patterns found in poems by famous poets into poem generation.

原文英語
主出版物標題Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
發行者IEEE Computer Society
ISBN(電子)9781728128160
DOIs
出版狀態已發佈 - 七月 2019
事件18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 - Kobe, 日本
持續時間: 七月 7 2019七月 10 2019

出版系列

名字Proceedings - International Conference on Machine Learning and Cybernetics
2019-July
ISSN(列印)2160-133X
ISSN(電子)2160-1348

會議

會議18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
國家日本
城市Kobe
期間7/7/197/10/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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  • 引用此

    Yeh, W. C., Chang, Y. C., Li, Y. H., & Chang, W. C. (2019). Rhyming Knowledge-Aware Deep Neural Network for Chinese Poetry Generation. 於 Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019 [8949208] (Proceedings - International Conference on Machine Learning and Cybernetics; 卷 2019-July). IEEE Computer Society. https://doi.org/10.1109/ICMLC48188.2019.8949208