Rhyme is deceptively intuitive: what is or is not a rhyme is constructed historically, scholars struggle with rhyme classification, and people disagree on whether two words are rhymed or not. This complicates automated rhymed recognition and evaluation, especially in multilingual context. This article investigates how much training data is needed for reliable unsupervised rhyme recognition using RhymeTagger, a language-independent tool that identifies rhymes based on repeating patterns in poetry corpora. We evaluate its performance across seven languages (Czech, German, English, French, Italian, Russian, and Slovene), examining how training size and language differences affect accuracy. To set a realistic performance benchmark, we assess inter-annotator agreement on a manually annotated subset of poems and analyze factors contributing to disagreement in expert annotations: phonetic similarity between rhyming words and their distance from each other in a poem. We also compare RhymeTagger to three large language models using a one-shot learning strategy. Our findings show that, once provided with sufficient training data, RhymeTagger consistently outperforms human agreement, while LLMs lacking phonetic representation significantly struggle with the task.

翻译：押韵具有一种令人迷惑的直觉性：什么构成押韵或不构成押韵是历史建构的结果，学者们对押韵分类存在分歧，而人们对于两个词是否押韵也常有不同看法。这给自动化押韵识别和评估带来了复杂性，尤其是在多语言语境下。本文探究了使用RhymeTagger（一种基于诗歌语料库中重复模式识别押韵的语言无关工具）进行可靠的无监督押韵识别需要多少训练数据。我们评估了该工具在七种语言（捷克语、德语、英语、法语、意大利语、俄语和斯洛文尼亚语）上的表现，考察了训练数据规模与语言差异对准确性的影响。为设定一个实际的性能基准，我们在一个手工标注的诗歌子集上评估了标注者之间的一致性，并分析了导致专家标注不一致的因素：押韵词之间的语音相似性以及它们在诗歌中的相互距离。我们还采用单样本学习策略，将RhymeTagger与三种大型语言模型进行了比较。研究结果表明，一旦提供足够的训练数据，RhymeTagger的表现始终优于人类一致性，而缺乏语音表征的大型语言模型在这一任务上则明显表现不佳。