{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:43Z","timestamp":1750309543090,"version":"3.41.0"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>Non-parallel story author-style transfer is an important but challenging task in natural language process, which requires transferring an input story into another author-style while maintaining source semantics. Despite recent progress, current text style transfer systems still face the challenges of low robustness of the model and low quality of the generated stories. To address these challenges, we propose an end-to-end framework incorporating dual encoder components and a fusion mechanism, which can achieve explicit style-content disentanglement and effectively fusing source-domain content with target-domain stylistic features. First, we extract text from source stories containing content information using empirical extraction rules and prompt engineering. And then, we propose a novel generation model which achieves story-style transfer through capturing source content features and target style features and then fusing them. We use two additional training objectives to learn high-level discourse representations. Moreover, we have constructed a new dataset for this task. Extensive experiments based on automatic and human evaluation show that our model significantly outperforms state-of-the-art baselines, achieving approximately 8.5% average improvement in comprehensive performance metrics, demonstrating the effectiveness of our model in story-style transfer.<\/jats:p>","DOI":"10.1145\/3726870","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T18:49:32Z","timestamp":1743187772000},"page":"1-26","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Non-Parallel Story Author-Style Transfer with Disentangled Representation Learning"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-4314-5878","authenticated-orcid":false,"given":"Hongbin","family":"Xia","sequence":"first","affiliation":[{"name":"Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0003-4303-2731","authenticated-orcid":false,"given":"Xiangzhong","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-2576-1426","authenticated-orcid":false,"given":"Yuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1082"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1601"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Joseph L. Fleiss. 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin 76 (1971) 378\u2013382. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:143544759","DOI":"10.1037\/h0031619"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11330"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00302"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.499"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.87"},{"key":"e_1_3_2_9_2","volume-title":"Advances in Neural Information Processing Systems","author":"Hinton Geoffrey E.","year":"2002","unstructured":"Geoffrey E. Hinton and Sam Roweis. 2002. Stochastic neighbor embedding. In Advances in Neural Information Processing Systems. S. Becker, S. Thrun, and K. Obermayer (Eds.), Vol. 15, MIT Press. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/proceedings.neurips.cc\/paper_files\/paper\/2002\/file\/6150ccc6069bea6b5716254057a194ef-Paper.pdf"},{"key":"e_1_3_2_10_2","first-page":"1729","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917)","author":"Hoffer Elad","year":"2017","unstructured":"Elad Hoffer, Itay Hubara, and Daniel Soudry. 2017. Train longer, generalize better: Closing the generalization gap in large batch training of neural networks. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917). Curran Associates Inc., Red Hook, NY, 1729\u20131739."},{"key":"e_1_3_2_11_2","unstructured":"Ari Holtzman Jan Buys Li Du Maxwell Forbes and Yejin Choi. 2020. The curious case of neural text degeneration. In International Conference on Learning Representations. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/openreview.net\/forum?id=rygGQyrFvH"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.138"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016554"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1041"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.215"},{"key":"e_1_3_2_16_2","unstructured":"Kalpesh Krishna Deepak Nathani Xavier Garc\u00eda Bidisha Samanta and Partha Pratim Talukdar. 2021. Few-shot controllable style transfer for low-resource settings: A study in Indian languages. arXiv:2110.07385. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:238857160"},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Kalpesh Krishna John Wieting and Mohit Iyyer. 2020. Reformulating unsupervised style transfer as paraphrase generation. arXiv:2010.05700. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:222291619","DOI":"10.18653\/v1\/2020.emnlp-main.55"},{"key":"e_1_3_2_18_2","unstructured":"Guillaume Lample Sandeep Subramanian Eric Michael Smith Ludovic Denoyer Marc\u2019Aurelio Ranzato and Y-Lan Boureau. 2018. Multiple-attribute text rewriting. In International Conference on Learning Representations. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:53334018"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.8"},{"key":"e_1_3_2_20_2","unstructured":"Haejun Lee Drew A. Hudson Kangwook Lee and Christopher D. Manning. 2020. SLM: Learning a discourse language representation with sentence unshuffling. arXiv:2010.16249. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:226222033"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11997"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1098"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00027"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073135"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_26_2","unstructured":"Alec Radford Jeff Wu Rewon Child David Luan Dario Amodei and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog 1 8 (2019) 9. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:160025533"},{"issue":"1","key":"e_1_3_2_27_2","first-page":"67","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research 21, 1, Article 140 (Jan. 2020), 67 pages.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1410"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295427"},{"key":"e_1_3_2_30_2","unstructured":"Bakhtiyar Syed Gaurav Verma Balaji Vasan Srinivasan Anandhavelu Natarajan and Vasudeva Varma. 2019. Adapting language models for non-parallel author-stylized rewriting. arXiv:1909.09962. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:202719307"},{"key":"e_1_3_2_31_2","unstructured":"Chen Tang Frank Guerin Yucheng Li and Chenghua Lin. 2022. Recent advances in neural text generation: A task-agnostic survey. arXiv:2203.03047. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/arxiv.org\/abs\/2203.03047"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/SLT.2018.8639573"},{"key":"e_1_3_2_33_2","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30, Curran Associates, Inc. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.27"},{"key":"e_1_3_2_35_2","first-page":"6426","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics","author":"Wang Xinpeng","year":"2022","unstructured":"Xinpeng Wang, Han Jiang, Zhihua Wei, and Shanlin Zhou. 2022. CHAE: Fine-grained controllable story generation with characters, actions and emotions. In Proceedings of the 29th International Conference on Computational Linguistics. Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, and Seung-Hoon Na (Eds.), International Committee on Computational Linguistics, Gyeongju, Republic of Korea, 6426\u20136435. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/aclanthology.org\/2022.coling-1.559"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/732"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2021.EMNLP-MAIN.195"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1090"},{"key":"e_1_3_2_39_2","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI \u201920)","author":"Yi Xiaoyuan","year":"2021","unstructured":"Xiaoyuan Yi, Zhenghao Liu, Wenhao Li, and Maosong Sun. 2021. Text style transfer via learning style instance supported latent space. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI \u201920), Article 526, 7 pages."},{"key":"e_1_3_2_40_2","volume-title":"International Conference on Learning Representations","author":"Zhang Tianyi","year":"2020","unstructured":"Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020. BERTScore: Evaluating text generation with BERT. In International Conference on Learning Representations. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/openreview.net\/forum?id=SkeHuCVFDr"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.827"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3726870","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/pdf\/10.1145\/3726870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:39Z","timestamp":1750295919000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3726870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,9]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,5,31]]}},"alternative-id":["10.1145\/3726870"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/3726870","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2025,5,9]]},"assertion":[{"value":"2024-05-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}