论文标题
POELM：无监督诗歌的仪表和押韵的语言模型
PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generation
论文作者
论文摘要
正式的诗歌诗对诗歌和韵律方案施加了严格的限制。大多数先前关于生成这种诗歌的工作都使用现有的诗进行监督，这对于大多数语言和诗歌形式都很难获得。在这项工作中，我们提出了一种无监督的方法，以遵循任何给定的仪表和韵律方案，而无需任何诗意的文字进行训练。我们的方法通过将常规的非精彩语料库分成短语，准备控制每个短语的长度和终端韵律并在增强语料库中训练变压器语言模型。在推断期间，我们为所需的仪表和韵律方案构建控制代码，并将我们的语言模型调节以产生正式的诗歌诗歌。西班牙和巴斯克的实验表明，我们的方法能够产生有效的诗歌，这些诗通常与人类所写的诗相媲美。
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.
