# MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space

arXiv: https://arxiv.org/abs/2003.06094 · HTML: https://mlsanity.com/paper/2003.06094/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 17 of 17)

- [Controllable Generation from Pre-trained Language Models via Inverse Prompting](https://mlsanity.com/paper/2103.10685/) (2021)
- [Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features](https://mlsanity.com/paper/2205.01821/) (2022)
- [Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation](https://mlsanity.com/paper/2207.06130/) (2022)
- [Evaluating Diversity in Automatic Poetry Generation](https://mlsanity.com/paper/2406.15267/) (2024)
- [Don't Go Far Off: An Empirical Study on Neural Poetry Translation](https://mlsanity.com/paper/2109.02972/) (2021)
- [LLM-based multi-agent poetry generation in non-cooperative environments](https://mlsanity.com/paper/2409.03659/) (2024)
- [PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation](https://mlsanity.com/paper/2306.08456/) (2023)
- [DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation](https://mlsanity.com/paper/2212.08724/) (2022)
- [Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness](https://mlsanity.com/paper/2210.14957/) (2022)
- [Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation](https://mlsanity.com/paper/2210.16557/) (2022)
- [Understanding Literary Texts by LLMs: A Case Study of Ancient Chinese Poetry](https://mlsanity.com/paper/2409.00060/) (2024)
- [Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment](https://mlsanity.com/paper/2412.20834/) (2024)
- [There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It](https://mlsanity.com/paper/2103.03775/) (2021)
- [Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation](https://mlsanity.com/paper/2210.12409/) (2022)
- [Poetry2Image: An Iterative Correction Framework for Images Generated from Chinese Classical Poetry](https://mlsanity.com/paper/2407.06196/) (2024)
- [WakaVT: A Sequential Variational Transformer for Waka Generation](https://mlsanity.com/paper/2104.00426/) (2021)
- [Topic-Guided Self-Introduction Generation for Social Media Users](https://mlsanity.com/paper/2305.15138/) (2023)

## Abstract (arXiv)

As an essential step towards computer creativity, automatic poetry generation has gained increasing attention these years. Though recent neural models make prominent progress in some criteria of poetry quality, generated poems still suffer from the problem of poor diversity. Related literature researches show that different factors, such as life experience, historical background, etc., would influence composition styles of poets, which considerably contributes to the high diversity of human-authored poetry. Inspired by this, we propose MixPoet, a novel model that absorbs multiple factors to create various styles and promote diversity. Based on a semi-supervised variational autoencoder, our model disentangles the latent space into some subspaces, with each conditioned on one influence factor by adversarial training. In this way, the model learns a controllable latent variable to capture and mix generalized factor-related properties. Different factor mixtures lead to diverse styles and hence further differentiate generated poems from each other. Experiment results on Chinese poetry demonstrate that MixPoet improves both diversity and quality against three state-of-the-art models.