@inproceedings{han-etal-2026-style,
title = "From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation",
author = "Han, Xueran and
Liu, Yuhan and
Li, Mingzhe and
Liu, Wei and
Hu, Sen and
Yan, Rui and
xu, Zhiqiang and
Chen, Xiuying",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.968/",
pages = "19391--19408",
ISBN = "979-8-89176-395-1",
abstract = "Great novels create immersive worlds with rich character arcs, well-structured plots, and nuanced writing styles. However, current novel generation methods often rely on brief, simplistic story outlines and generate details using plain, generic language.To bridge this gap, we introduce the task of $\textit{Imitative Novel Generation}$, which requires the generated novels to imitate the distinctive features of the original work, including understanding character profiles and world views, predicting plausible plot developments, and writing concrete details using vivid, expressive language.To achieve this, we propose WriterAgent, a novel generation system designed to master the core aspects of literary imitative.WriterAgent is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence. Its key tasks include language style learning, character modeling, plot planning, and stylish writing, ensuring comprehensive narrative control.To support this, WriterAgent leverages the WriterLoRA framework, an extension of LoRA with hierarchical and cumulative task-specific modules, each specializing in a different narrative aspect. We evaluate WriterAgent on multilingual classics like $\textit{Harry Potter}$ and $\textit{Dream of the Red Chamber}$, demonstrating its superiority over baselines in capturing the target author{'}s settings, character dynamics, and writing style to produce coherent, faithful narratives.We hope this work inspires literary creativity in NLP: ${WriterAgent}$."
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<abstract>Great novels create immersive worlds with rich character arcs, well-structured plots, and nuanced writing styles. However, current novel generation methods often rely on brief, simplistic story outlines and generate details using plain, generic language.To bridge this gap, we introduce the task of Imitative Novel Generation, which requires the generated novels to imitate the distinctive features of the original work, including understanding character profiles and world views, predicting plausible plot developments, and writing concrete details using vivid, expressive language.To achieve this, we propose WriterAgent, a novel generation system designed to master the core aspects of literary imitative.WriterAgent is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence. Its key tasks include language style learning, character modeling, plot planning, and stylish writing, ensuring comprehensive narrative control.To support this, WriterAgent leverages the WriterLoRA framework, an extension of LoRA with hierarchical and cumulative task-specific modules, each specializing in a different narrative aspect. We evaluate WriterAgent on multilingual classics like Harry Potter and Dream of the Red Chamber, demonstrating its superiority over baselines in capturing the target author’s settings, character dynamics, and writing style to produce coherent, faithful narratives.We hope this work inspires literary creativity in NLP: WriterAgent.</abstract>
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%0 Conference Proceedings
%T From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation
%A Han, Xueran
%A Liu, Yuhan
%A Li, Mingzhe
%A Liu, Wei
%A Hu, Sen
%A Yan, Rui
%A xu, Zhiqiang
%A Chen, Xiuying
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F han-etal-2026-style
%X Great novels create immersive worlds with rich character arcs, well-structured plots, and nuanced writing styles. However, current novel generation methods often rely on brief, simplistic story outlines and generate details using plain, generic language.To bridge this gap, we introduce the task of Imitative Novel Generation, which requires the generated novels to imitate the distinctive features of the original work, including understanding character profiles and world views, predicting plausible plot developments, and writing concrete details using vivid, expressive language.To achieve this, we propose WriterAgent, a novel generation system designed to master the core aspects of literary imitative.WriterAgent is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence. Its key tasks include language style learning, character modeling, plot planning, and stylish writing, ensuring comprehensive narrative control.To support this, WriterAgent leverages the WriterLoRA framework, an extension of LoRA with hierarchical and cumulative task-specific modules, each specializing in a different narrative aspect. We evaluate WriterAgent on multilingual classics like Harry Potter and Dream of the Red Chamber, demonstrating its superiority over baselines in capturing the target author’s settings, character dynamics, and writing style to produce coherent, faithful narratives.We hope this work inspires literary creativity in NLP: WriterAgent.
%U https://aclanthology.org/2026.findings-acl.968/
%P 19391-19408
Markdown (Informal)
[From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation](https://aclanthology.org/2026.findings-acl.968/) (Han et al., Findings 2026)
ACL
- Xueran Han, Yuhan Liu, Mingzhe Li, Wei Liu, Sen Hu, Rui Yan, Zhiqiang xu, and Xiuying Chen. 2026. From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19391–19408, San Diego, California, United States. Association for Computational Linguistics.