| Challenge: | Existing models for classical Chinese poetry generation only allow users to use keywords to interfere with the meaning of generated poems. |
| Approach: | They propose a model to generate classical Chinese poems from vernacular . their model uses unsupervised machine translation to generate Chinese poems . human evaluation shows it can generate high-quality poems comparable to amateur poems - authors . |
| Outcome: | The proposed model improves the perplexity and BLEU of the proposed model compared with typical models and human evaluation shows it generates high-quality poems comparable to amateur poems. |
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| Challenge: | Chinese classical poetry is one of the most valuable literary and cultural heritages of China . it has many particular characteristics in its language structure, ranging from form, sound to meaning . a proposed uniformed framework for generating major types of Chinese classical poems is proposed . |
| Approach: | They propose a uniformed framework for generating major types of Chinese classical poems . they use a form- stressed weighting method to strengthen the control to the form of the generated poems a proposed framework is incorporated into Jiuge, the most influential Chinese classical poetry generation system developed by Tsinghua University. |
| Outcome: | The proposed framework can generate Chinese classical poems of major types with high quality in form and content. |
Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement (D18-1)
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| Challenge: | Automatic Chinese poetry generation is one of the first attempts towards computer writing. |
| Approach: | They propose a model which requires no supervised style labeling to generate stylistic poems . they incorporate mutual information, a concept in information theory, into modeling . |
| Outcome: | The proposed model generates stylistic poems without losing fluency and coherency . it is based on mutual information, a concept in information theory . |
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training (D18-1)
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| Challenge: | Existing models for automatic poetry generation lack term novelty and thematic consistency. |
| Approach: | They propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation. |
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Lingxi: A Diversity-aware Chinese Modern Poetry Generation System (2023.acl-demo)
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| Challenge: | Chinese modern poetry generation is a challenging task because of the word segmentation problem and decoding methods . the decoding method may induce repetition and boredom and lower the diversity of generated poetry. |
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Automatic Poetry Generation from Prosaic Text (2020.acl-main)
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| Challenge: | In recent years, successful approaches have emerged to accurately model various aspects of natural language. |
| Approach: | They propose to combine neural networks with a poetry generation system that only uses standard text as input . they use standard text to model syntactic well-formedness and topical coherence . |
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TopWORDS-Poetry: Simultaneous Text Segmentation and Word Discovery for Classical Chinese Poetry via Bayesian Inference (2023.emnlp-main)
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| Challenge: | Experimental studies confirm that TopWORDS-Poetry can successfully segment poetry words without pre-given vocabulary or training corpus. |
| Approach: | They propose an unsupervised method that can achieve reliable text segmentation and word discovery for classical Chinese poetry simultaneously without pre-given vocabulary or training corpus. |
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Large-Scale Corpus Construction and Retrieval-Augmented Generation for Ancient Chinese Poetry: New Method and Data Insights (2025.findings-naacl)
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| Challenge: | Ancient Chinese poetry presents unique challenges for Large Language Models due to data scarcity and limited ability of general LLMs when dealing with ACP. |
| Approach: | They propose a specialized Retrieval-Augmented Generation framework to improve LLMs' performance . they use 1.1 million ancient poems and 990K related texts to address hallucination issues . |
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Yu Sheng: Human-in-Loop Classical Chinese Poetry Generation System (2023.eacl-demo)
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| Challenge: | Existing systems for poetry generation are not flexible in polishing and customization. |
| Approach: | They propose a web-based poetry generation system that provides customization options for users with different backgrounds to engage in the process of poetry composition. |
| Outcome: | The proposed system can generate and polish classical Chinese poetry compared to other vanilla models. |
ByGPT5: End-to-End Style-conditioned Poetry Generation with Token-free Language Models (2023.acl-long)
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| Challenge: | End-to-end models learn to complete a task by directly learning all steps, without intermediary algorithms such as hand-crafted rules or post-processing. |
| Approach: | They propose to train end-to-end poetry generation conditioned on styles such as rhyme, meter, and alliteration . they pre-train ByGPT5, a new token-free decoder-only language model, and fine-tune it on a custom corpus of English and German quatrains . |
| Outcome: | The proposed model outperforms other models on a large custom corpus of English and German quatrains while being more parameter efficient and performing favorably compared to humans. |
Automatic Poetry Generation with Mutual Reinforcement Learning (D18-1)
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| Challenge: | Existing models for automatic poetry generation are based on maximum likelihood estimation (MLE) MLE-based models tend to remember common patterns of the poetry corpus, which results in loss-evaluation mismatch. |
| Approach: | They propose to model the criteria and use them as explicit rewards to guide gradient update by reinforcement learning to motivate the model to pursue higher scores. |
| Outcome: | The proposed model outperforms the current state-of-the-art model and improves on Chinese poetry. |