# PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation

arXiv: https://arxiv.org/abs/2306.08456 · HTML: https://mlsanity.com/paper/2306.08456/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Discrete and text diffusion, one of 14 directions in Diffusion models.
- Builds on 29 of its 46 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Placed in

- Diffusion models / [Discrete and text diffusion](https://mlsanity.com/diffusion/discrete-and-text-diffusion/)

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models](https://mlsanity.com/paper/2103.04922/) (2021)
- [Controlled Text Generation with Natural Language Instructions](https://mlsanity.com/paper/2304.14293/) (2023)
- [SummIt: Iterative Text Summarization via ChatGPT](https://mlsanity.com/paper/2305.14835/) (2023)
- [Adversarial Ranking for Language Generation](https://mlsanity.com/paper/1705.11001/) (2017)
- [ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer](https://mlsanity.com/paper/2306.07799/) (2023)
- [SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint](https://mlsanity.com/paper/2012.05168/) (2020)
- [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks](https://mlsanity.com/paper/1702.07983/) (2017)
- [Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme](https://mlsanity.com/paper/1807.03491/) (2018)
- [DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation](https://mlsanity.com/paper/2210.09551/) (2022)
- [SongNet: Rigid Formats Controlled Text Generation](https://mlsanity.com/paper/2004.08022/) (2020)
- [Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features](https://mlsanity.com/paper/2205.01821/) (2022)
- [MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space](https://mlsanity.com/paper/2003.06094/) (2020)
- [An Iterative Polishing Framework based on Quality Aware Masked Language Model for Chinese Poetry Generation](https://mlsanity.com/paper/1911.13182/) (2019)
- [Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation](https://mlsanity.com/paper/1811.05067/) (2018)

## Built on by (most cited first, 7 of 7)

- [A Survey on Diffusion Language Models](https://mlsanity.com/paper/2508.10875/) (2025)
- [Discrete Diffusion in Large Language and Multimodal Models: A Survey](https://mlsanity.com/paper/2506.13759/) (2025)
- [Don't Let It Fade: Preserving Edits in Diffusion Language Models via Token Timestep Allocation](https://mlsanity.com/paper/2510.26200/) (2025)
- [Automated Evaluation of Meter and Rhyme in Russian Generative and Human-Authored Poetry](https://mlsanity.com/paper/2502.20931/) (2025)
- [Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts](https://mlsanity.com/paper/2505.04507/) (2025)
- [Detecting and Guiding LLM-Generated Korean Poetry with Interpretable Form-level Features](https://mlsanity.com/paper/2608.28986/) (2026)
- [Single-Sample Decoding of a Two-Layer Sound-Word Canvas Model for Thai Klon Pad](https://mlsanity.com/paper/2610.14980/) (2026)

## Abstract (arXiv)

Controllable text generation is a challenging and meaningful field in natural language generation (NLG). Especially, poetry generation is a typical one with well-defined and strict conditions for text generation which is an ideal playground for the assessment of current methodologies. While prior works succeeded in controlling either semantic or metrical aspects of poetry generation, simultaneously addressing both remains a challenge. In this paper, we pioneer the use of the Diffusion model for generating sonnets and Chinese SongCi poetry to tackle such challenges. In terms of semantics, our PoetryDiffusion model, built upon the Diffusion model, generates entire sentences or poetry by comprehensively considering the entirety of sentence information. This approach enhances semantic expression, distinguishing it from autoregressive and large language models (LLMs). For metrical control, the separation feature of diffusion generation and its constraint control module enable us to flexibly incorporate a novel metrical controller to manipulate and evaluate metrics (format and rhythm). The denoising process in PoetryDiffusion allows for gradual enhancement of semantics and flexible integration of the metrical controller which can calculate and impose penalties on states that stray significantly from the target control distribution. Experimental results on two datasets demonstrate that our model outperforms existing models in automatic evaluation of semantic, metrical, and overall performance as well as human evaluation.