# Chinese Poetry Generation with a Salient-Clue Mechanism

arXiv: https://arxiv.org/abs/1809.04313 · HTML: https://mlsanity.com/paper/1809.04313/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [PoeTone: A Framework for Constrained Generation of Structured Chinese Songci with LLMs](https://mlsanity.com/paper/2508.02515/) (2025)
- [POEMetric: The Last Stanza of Humanity](https://mlsanity.com/paper/2604.03695/) (2026)
- [An Adversarial Multi-Task Learning Method for Chinese Text Correction with Semantic Detection](https://mlsanity.com/paper/2306.16313/) (2023)
- [Jiuge-Tuiqiao: An Interpretable Human-AI System for Classical Chinese Poetry Refinement](https://mlsanity.com/paper/2608.23098/) (2026)

## Abstract (arXiv)

As a precious part of the human cultural heritage, Chinese poetry has influenced people for generations. Automatic poetry composition is a challenge for AI. In recent years, significant progress has been made in this area benefiting from the development of neural networks. However, the coherence in meaning, theme or even artistic conception for a generated poem as a whole still remains a big problem. In this paper, we propose a novel Salient-Clue mechanism for Chinese poetry generation. Different from previous work which tried to exploit all the context information, our model selects the most salient characters automatically from each so-far generated line to gradually form a salient clue, which is utilized to guide successive poem generation process so as to eliminate interruptions and improve coherence. Besides, our model can be flexibly extended to control the generated poem in different aspects, for example, poetry style, which further enhances the coherence. Experimental results show that our model is very effective, outperforming three strong baselines.