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. |
| Approach: | They propose a Chinese word segmentation-based decoding system that incorporates Chinese word segments into tokenization. |
| Outcome: | The proposed system can achieve high vocabulary coverage rate with a reasonable vocabulary size. |
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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 . |
Evaluating Diversity in Automatic Poetry Generation (2024.emnlp-main)
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| Challenge: | Existing models for creative text generation are not evaluated regarding how different generated poems are from existing training sets. |
| Approach: | They evaluate the diversity of automatically generated poetry by comparing distributions of generated poetry to distributions in human poetry along structural, lexical, semantic and stylistic dimensions. |
| Outcome: | The proposed model types show that style-conditioning and character-level modeling increases diversity across virtually all dimensions. |
Benchmarking the Detection of LLMs-Generated Modern Chinese Poetry (2025.findings-emnlp)
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| Challenge: | Detecting AI-generated poetry is difficult due to distinctive characteristics of modern Chinese poetry. |
| Approach: | They propose a benchmark for detecting AI-generated modern Chinese poetry . they use a high-quality dataset and systematic performance assessments . |
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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. |
PoemBERT: A Dynamic Masking Content and Ratio Based Semantic Language Model For Chinese Poem Generation (2025.coling-main)
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| Challenge: | Despite the lack of pre-trained models for ancient Chinese poetry, the unique artistry and structural nuances of Chinese poetry present complex challenges for machine learning in creative applications. |
| Approach: | They propose a BERT-based model incorporating sentiment and pinyin embeddings into the model, enhancing its sensitivity to emotional information and addressing challenges posed by the phenomenon of multiple pronunciations for the same Chinese character. |
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Generating Classical Chinese Poems from Vernacular Chinese (D19-1)
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| 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 . |
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Capabilities and Evaluation Biases of Large Language Models in Classical Chinese Poetry Generation: A Case Study on Tang Poetry (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly applied to creative domains, yet performance in classical Chinese poetry generation and evaluation remains poorly understood. |
| Approach: | They propose a framework that combines computational metrics, LLM-as-a-judge assessment, and human expert validation to evaluate large language models. |
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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. |
| Outcome: | Experimental results show that TopWORDS-Poetry can segment poetry lines into meaningful words with high quality without pre-given vocabulary or training corpus. |
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 . |
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Scansion-based Lyrics Generation (2024.lrec-main)
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| Challenge: | a new method for generating lyrics for Mandarin songs is based on scansion . the number of syllables required is variable due to the number and number of notes . |
| Approach: | They propose a method to generate Mandarin lyrics with a good match between melody and tonal contour. |
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