Poetry generation is a typical and popular task in natural language
generation. While prior works have shown success in controlling either semantic
or metrical aspects of poetry generation, there are still challenges in
addressing both perspectives simultaneously. In this paper, we employ the
Diffusion model to generate poetry in Sonnet and SongCi in Chinese for the
first time to tackle such challenges. Different from autoregressive generation,
our PoetryDiffusion model, based on Diffusion model, generates the complete
sentence or poetry by taking into account the whole sentence information,
resulting in improved semantic expression. Additionally, we 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. Experimental
results on two datasets demonstrate that our model outperforms existing models
in terms of semantic, metrical and overall performance.

本文利用扩散模型分别在中文诗歌的 “宋词” 和 “十四行诗” 上进行了难点挑战的同时，结合了特殊的度量控制方法，提出一种新的自然语言生成模型，并得出了在两个数据集上的实验结果，表明我们的模型在语义、度量和整体性能方面优于现有模型。