| 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. |
Similar Papers
Automatic Poetry Generation from Prosaic Text (2020.acl-main)
Copied to clipboard
| 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 . |
| Outcome: | The proposed framework is applied to the generation of poems in English and French . it uses standard, non-poetic text and its output is constrained to confer a poetic character . |
Evaluating Diversity in Automatic Poetry Generation (2024.emnlp-main)
Copied to clipboard
| 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. |
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training (D18-1)
Copied to clipboard
| 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. |
| Outcome: | The proposed model outperforms existing models on a large poetry corpus on 'classical Chinese' . it generates poems with novel terms and learns their thematic consistency with their titles. |
Deep-speare: A joint neural model of poetic language, meter and rhyme (P18-1)
Copied to clipboard
| Challenge: | a recent surge of interest in deep learning has led to creative applications for poetry generation . a novel joint architecture captures language, rhyme and meter for sonnet modelling . |
| Approach: | They propose a joint architecture that captures language, rhyme and meter for sonnet modelling. |
| Outcome: | The proposed architecture captures language, rhyme and meter for sonnet modelling. |
Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement (D18-1)
Copied to clipboard
| 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 Modern Poetry Automatically in Finnish (D19-1)
Copied to clipboard
| Challenge: | a novel approach to generate poetry for the morphologically rich Finnish language is presented . the method is evaluated and described within the paradigm of computational creativity . |
| Approach: | They propose a method for generating poetry automatically for the morphologically rich Finnish language using a genetic algorithm. |
| Outcome: | The proposed method improves the state-of-the-art of previous Finnish poetry generators by introducing a higher degree of freedom in terms of structural creativity. |
Constrained Language Models for Interactive Poem Generation (2022.lrec-1)
Copied to clipboard
Andrei Popescu-Belis, Àlex Atrio, Valentin Minder, Aris Xanthos, Gabriel Luthier, Simon Mattei, Antonio Rodriguez
| Challenge: | Neural language models cannot learn constraints from data, which is scarce for a well-resourced language such as French. |
| Approach: | They propose a system that combines neural language models with constraints that can be set by users on form, topic, emotion, and rhyming scheme. |
| Outcome: | The proposed system generates poems and stanzas using LMs and rule-based algorithms . it has been demonstrated at public events and log analysis shows that users found it engaging . |
Acrostic Poem Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Acrostic poems contain a hidden message; typically, the first letter of each line spells out a word or short phrase. |
| Approach: | They propose a task for acrostic poem generation in English with multiple constraints . they define the task as a generation task with multiple constraint constraints based on a conditional neural language model and a neural rhyming model . |
| Outcome: | The proposed task is based on a baseline model and a neural rhyming model. |
Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System (P19-3)
Copied to clipboard
Guo Zhipeng, Xiaoyuan Yi, Maosong Sun, Wenhao Li, Cheng Yang, Jiannan Liang, Huimin Chen, Yuhui Zhang, Ruoyu Li
| Challenge: | Existing systems for automatic poetry generation are model-oriented, resulting in poor user participation. |
| Approach: | They propose a human-machine collaborative Chinese classical poetry generation system called Jiuge . Jiuge allows users to revise unsatisfied parts of a generated poem draft repeatedly . |
| Outcome: | The proposed system allows users to revise unsatisfied parts of a generated poem draft repeatedly. |
Capabilities and Evaluation Biases of Large Language Models in Classical Chinese Poetry Generation: A Case Study on Tang Poetry (2026.findings-acl)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework evaluates state-of-the-art LLMs across multiple dimensions of poetic quality in Tang poetry generation. |