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

PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generation