{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:22:59Z","timestamp":1783610579262,"version":"3.55.0"},"reference-count":21,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:00:00Z","timestamp":1687132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61702080, 62076046, 62006130, 62066044"],"award-info":[{"award-number":["61702080, 62076046, 62006130, 62066044"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Major Science and Technology Projects of Yunnan Province","award":["202002ab080001-1"],"award-info":[{"award-number":["202002ab080001-1"]}]},{"name":"Inner Mongolia Science Foundation","award":["2022MS06028"],"award-info":[{"award-number":["2022MS06028"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>\n            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\n            <jats:italic>TPoet<\/jats:italic>\n            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.\n          <\/jats:p>","DOI":"10.1145\/3593805","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T12:23:21Z","timestamp":1682079801000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["TPoet: Topic-Enhanced Chinese Poetry Generation"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5557-7515","authenticated-orcid":false,"given":"Liang","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6346-3868","authenticated-orcid":false,"given":"Zhexu","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6434-7247","authenticated-orcid":false,"given":"Fengqing","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0872-7688","authenticated-orcid":false,"given":"Hongfei","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7895-2763","authenticated-orcid":false,"given":"Junpeng","family":"Li","sequence":"additional","affiliation":[{"name":"Sino-Platinum Metals Co., Ltd., China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,6,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_3_2_3_2","first-page":"4925","volume-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence","author":"Chen Huimin","year":"2019","unstructured":"Huimin Chen, Xiaoyuan Yi, Maosong Sun, Wenhao Li, Cheng Yang, and Zhipeng Guo. 2019. Sentiment-controllable Chinese poetry generation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 4925\u20134931."},{"key":"e_1_3_2_4_2","first-page":"7643","volume-title":"Proceedings of the 24th AAAI Conference on Artificial Intelligence","author":"Deng Liming","year":"2020","unstructured":"Liming Deng, Jie Wang, Hang-Ming Liang, Hui Chen, Zhiqiang Xie, Bojin Zhuang, Shaojun Wang, and Jing Xiao. 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. 7643\u20137650. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6265."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_6_2","first-page":"38","article-title":"Chinese new rhyme (the fourteen rhymes)","volume":"5","author":"Poetry Editorial Department of Chinese","year":"2004","unstructured":"Editorial Department of Chinese Poetry. 2004. Chinese new rhyme (the fourteen rhymes). Chinese Poetry 5 (2004), 38\u201351.","journal-title":"Chinese Poetry"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1082"},{"key":"e_1_3_2_8_2","first-page":"1650","volume-title":"Proceedings of the 26th AAAI Conference on Artificial Intelligence","author":"He Jing","year":"2012","unstructured":"Jing He, Ming Zhou, and Long Jiang. 2012. Generating Chinese classical poems with statistical machine translation models. In Proceedings of the 26th AAAI Conference on Artificial Intelligence. 1650\u20131656."},{"key":"e_1_3_2_9_2","volume-title":"arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1014"},{"key":"e_1_3_2_11_2","first-page":"3890","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Li Juntao","year":"2018","unstructured":"Juntao Li, Yan Song, Haisong Zhang, Dongmin Chen, Shuming Shi, Dongyan Zhao, and Rui Yan. 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. 3890\u20133900."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.68"},{"key":"e_1_3_2_13_2","volume-title":"Improving Language Understanding by Generative Pre-Training","author":"Radford Alec","year":"2018","unstructured":"Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving Language Understanding by Generative Pre-Training. Technical Report. OpenAI."},{"key":"e_1_3_2_14_2","first-page":"2197","volume-title":"Proceedings of the 22nd International Joint Conference on Artificial Intelligence","author":"Rui Yan","year":"2013","unstructured":"Yan Rui, Jiang Han, Mirella Lapata, Shou De Lin, Xueqiang Lv, and Xiaoming Li. 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. 2197\u20132203."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0140525X16001837"},{"key":"e_1_3_2_16_2","first-page":"1051","volume-title":"Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers","author":"Wang Zhe","year":"2016","unstructured":"Zhe Wang, Wei He, Hua Wu, Haiyang Wu, Wei Li, Haifeng Wang, and Enhong Chen. 2016. Chinese poetry generation with planning based neural network. In Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers. 1051\u20131060."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304401"},{"key":"e_1_3_2_18_2","article-title":"Generating Chinese classical poems with RNN encoder-decoder","author":"Yi Xiaoyuan","year":"2016","unstructured":"Xiaoyuan Yi, Ruoyu Li, and Maosong Sun. 2016. Generating Chinese classical poems with RNN encoder-decoder. arXiv preprint arXiv:1604.01537 (2016).","journal-title":"arXiv preprint arXiv:1604.01537"},{"key":"e_1_3_2_19_2","first-page":"3143","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Yi Xiaoyuan","year":"2018","unstructured":"Xiaoyuan Yi, Maosong Sun, Ruoyu Li, and Wenhao Li. 2018. Automatic poetry generation with mutual reinforcement learning. 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