State-of-the-art poetry generation systems are often complex. They either consist of task-specific model pipelines, incorporate prior knowledge in the form of manually created constraints or both. In contrast, end-to-end models would not suffer from the overhead of having to model prior knowledge and could learn the nuances of poetry from data alone, reducing the degree of human supervision required. In this work, we investigate end-to-end poetry generation conditioned on styles such as rhyme, meter, and alliteration. We identify and address lack of training data and mismatching tokenization algorithms as possible limitations of past attempts. In particular, we successfully pre-train and release ByGPT5, a new token-free decoder-only language model, and fine-tune it on a large custom corpus of English and German quatrains annotated with our styles. We show that ByGPT5 outperforms other models such as mT5, ByT5, GPT-2 and ChatGPT, while also being more parameter efficient and performing favorably compared to humans. In addition, we analyze its runtime performance and introspect the model's understanding of style conditions. We make our code, models, and datasets publicly available.

本文介绍了一种以韵脚、节律和押韵等风格为条件，使用深度学习模型生成诗歌的方法，通过先预训练自然语言处理模型ByGPT5，再在英语和德语四行诗的自定义语料库上进行微调，实现了较高的生成效果和参数效率，从而提高了生成诗歌的成果和可行性。

使用无记号语言模型实现端到端的有风格限制的诗歌生成