Formal verse poetry imposes strict constraints on the meter and rhyme scheme
of poems. Most prior work on generating this type of poetry uses existing poems
for supervision, which are difficult to obtain for most languages and poetic
forms. In this work, we propose an unsupervised approach to generate poems
following any given meter and rhyme scheme, without requiring any poetic text
for training. Our method works by splitting a regular, non-poetic corpus into
phrases, prepending control codes that describe the length and end rhyme of
each phrase, and training a transformer language model in the augmented corpus.
During inference, we build control codes for the desired meter and rhyme
scheme, and condition our language model on them to generate formal verse
poetry. Experiments in Spanish and Basque show that our approach is able to
generate valid poems, which are often comparable in quality to those written by
humans.

本研究提出了一种无监督的方法来生成符合任何指定的韵律和押韵方案的诗歌，利用变形器语言模型通过在常规的非诗歌语料库中拆分短语，并在每个短语前添加描述其长度和结束韵的控制代码进行训练。实验表明，这种方法能够生成有效的诗歌，并且与人类写的诗歌在质量上相当。