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TPoet: Topic-Enhanced Chinese Poetry Generation

Published:19 June 2023Publication History
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Abstract

Chinese poetry generation has been a challenging part of natural language processing due to the unique literariness and aesthetics of poetry. In most cases, the content of poetry is topic related. In other words, specific thoughts or emotions are usually expressed regarding given topics. However, topic information is rarely taken into consideration in current studies about poetry generation models. In this article, we propose a topic-enhanced Chinese poetry generation model called TPoet in which the topic model is integrated into the Transformer-based auto-regressive text generation model. By feeding topic information to the input layer and heterogeneous attention mechanism, TPoet can implicitly learn the latent information of topic distribution. In addition, by setting multiple identifiers such as segment, rhyme, and tone, the model can explicitly learn the constraints of generated poems. Extensive experimental results show that the quality of TPoet-generated poems outperforms the current advanced models or systems, and the topic consistency and diversity in generated poems have been significantly improved as well.

REFERENCES

  1. [1] Blei David M., Ng Andrew Y., and Jordan Michael I.. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3 (2003), 9931022.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Chen Huimin, Yi Xiaoyuan, Sun Maosong, Li Wenhao, Yang Cheng, and Guo Zhipeng. 2019. Sentiment-controllable Chinese poetry generation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 49254931.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Deng Liming, Wang Jie, Liang Hang-Ming, Chen Hui, Xie Zhiqiang, Zhuang Bojin, Wang Shaojun, and Xiao Jing. 2020. An iterative polishing framework based on quality aware masked language model for Chinese poetry generation. In Proceedings of the 24th AAAI Conference on Artificial Intelligence. 76437650. https://aaai.org/ojs/index.php/AAAI/article/view/6265.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Devlin Jacob, Chang Ming-Wei, Lee Kenton, and Toutanova Kristina. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 41714186. Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Poetry Editorial Department of Chinese. 2004. Chinese new rhyme (the fourteen rhymes). Chinese Poetry 5 (2004), 3851.Google ScholarGoogle Scholar
  6. [6] Fan Angela, Lewis Mike, and Dauphin Yann. 2018. Hierarchical neural story generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 889898. Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] He Jing, Zhou Ming, and Jiang Long. 2012. Generating Chinese classical poems with statistical machine translation models. In Proceedings of the 26th AAAI Conference on Artificial Intelligence. 16501656.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. [8] Kingma Diederik P. and Ba Jimmy. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google ScholarGoogle Scholar
  9. [9] Li Jiwei, Galley Michel, Brockett Chris, Gao Jianfeng, and Dolan Bill. 2016. A diversity-promoting objective function for neural conversation models. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 110119. Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Li Juntao, Song Yan, Zhang Haisong, Chen Dongmin, Shi Shuming, Zhao Dongyan, and Yan Rui. 2018. Generating classical Chinese poems via conditional variational autoencoder and adversarial training. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 38903900.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Li Piji, Zhang Haisong, Liu Xiaojiang, and Shi Shuming. 2020. Rigid formats controlled text generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 742751. Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Radford Alec, Narasimhan Karthik, Salimans Tim, and Sutskever Ilya. 2018. Improving Language Understanding by Generative Pre-Training. Technical Report. OpenAI.Google ScholarGoogle Scholar
  13. [13] Rui Yan, Han Jiang, Lapata Mirella, Lin Shou De, Lv Xueqiang, and Li Xiaoming. 2013. i, Poet: Automatic Chinese poetry composition through a generative summarization framework under constrained optimization. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 21972203.Google ScholarGoogle Scholar
  14. [14] Vaswani Ashish, Shazeer Noam, Parmar Niki, Uszkoreit Jakob, Jones Llion, Gomez Aidan N., Kaiser Lukasz, and Polosukhin Illia. 2017. Attention is all you need. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS’17). 5998–6008. Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Wang Zhe, He Wei, Wu Hua, Wu Haiyang, Li Wei, Wang Haifeng, and Chen Enhong. 2016. Chinese poetry generation with planning based neural network. In Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers. 10511060.Google ScholarGoogle Scholar
  16. [16] Yang Xiaopeng, Lin Xiaowen, Suo Shunda, and Li Ming. 2018. Generating thematic Chinese poetry using conditional variational autoencoders with hybrid decoders. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 45394545.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Yi Xiaoyuan, Li Ruoyu, and Sun Maosong. 2016. Generating Chinese classical poems with RNN encoder-decoder. arXiv preprint arXiv:1604.01537 (2016).Google ScholarGoogle Scholar
  18. [18] Yi Xiaoyuan, Sun Maosong, Li Ruoyu, and Li Wenhao. 2018. Automatic poetry generation with mutual reinforcement learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 31433153.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Zhang Jiyuan, Feng Yang, Wang Dong, Wang Yang, Abel Andrew, Zhang Shiyue, and Zhang Andi. 2017. Flexible and creative Chinese poetry generation using neural memory. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 13641373.Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Zhang Xingxing and Lapata Mirella. 2014. Chinese poetry generation with recurrent neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 670680.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Zhou Changle, You Wei, and Ding Xiaojun. 2010. Genetic algorithm and its implementation of automatic generation of Chinese SONGCI. Journal of Software 21, 3 (2010), 427437. Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 6
      June 2023
      635 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3604597
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 19 June 2023
      • Online AM: 21 April 2023
      • Accepted: 2 April 2023
      • Revised: 20 January 2023
      • Received: 26 July 2022
      Published in tallip Volume 22, Issue 6

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