@inproceedings{han-etal-2026-jiuge,
title = "Jiuge-Tuiqiao: An Interpretable Human-{AI} System for Classical {C}hinese Poetry Refinement",
author = "Han, Yufeng and
Deng, Lifan and
Kong, Cunliang and
Li, Wenhao and
Cong, Xin and
Bai, Yuzhuo and
Luo, Kangyang and
Sun, Maosong",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.5/",
pages = "46--55",
ISBN = "979-8-89176-461-3",
abstract = "Classical Chinese poetry composition has long valued *Tuiqiao*, the iterative refinement of words, imagery, and prosody. However, many current AI poetry systems follow a one-shot generation paradigm, which reduces users to prompt providers and weakens their creative agency. We present *Jiuge-Tuiqiao*, an interactive human-AI collaborative system for classical Chinese poetry composition. The system is designed around a triadic model: user-driven control, ancient-guided evidence, and AI-assisted generation. Users can lock characters or lines, receive real-time prosody feedback, and obtain interpretable refinement suggestions grounded in high-frequency collocations, PPL-ranked classical lines, and structured knowledge extracted from classical encyclopedias. This design turns AI from an autonomous generator into a background assistant that supports the user{'}s own process of poetic refinement. Preliminary experiments and user feedback suggest that *Jiuge-Tuiqiao* improves controllability, interpretability, and user engagement in classical poetry composition."
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%0 Conference Proceedings
%T Jiuge-Tuiqiao: An Interpretable Human-AI System for Classical Chinese Poetry Refinement
%A Han, Yufeng
%A Deng, Lifan
%A Kong, Cunliang
%A Li, Wenhao
%A Cong, Xin
%A Bai, Yuzhuo
%A Luo, Kangyang
%A Sun, Maosong
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F han-etal-2026-jiuge
%X Classical Chinese poetry composition has long valued *Tuiqiao*, the iterative refinement of words, imagery, and prosody. However, many current AI poetry systems follow a one-shot generation paradigm, which reduces users to prompt providers and weakens their creative agency. We present *Jiuge-Tuiqiao*, an interactive human-AI collaborative system for classical Chinese poetry composition. The system is designed around a triadic model: user-driven control, ancient-guided evidence, and AI-assisted generation. Users can lock characters or lines, receive real-time prosody feedback, and obtain interpretable refinement suggestions grounded in high-frequency collocations, PPL-ranked classical lines, and structured knowledge extracted from classical encyclopedias. This design turns AI from an autonomous generator into a background assistant that supports the user’s own process of poetic refinement. Preliminary experiments and user feedback suggest that *Jiuge-Tuiqiao* improves controllability, interpretability, and user engagement in classical poetry composition.
%U https://aclanthology.org/2026.emnlp-demos.5/
%P 46-55
Markdown (Informal)
[Jiuge-Tuiqiao: An Interpretable Human-AI System for Classical Chinese Poetry Refinement](https://aclanthology.org/2026.emnlp-demos.5/) (Han et al., EMNLP 2026)
ACL
- Yufeng Han, Lifan Deng, Cunliang Kong, Wenhao Li, Xin Cong, Yuzhuo Bai, Kangyang Luo, and Maosong Sun. 2026. Jiuge-Tuiqiao: An Interpretable Human-AI System for Classical Chinese Poetry Refinement. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 46–55, Budapest, Hungary. Association for Computational Linguistics.