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