Metonymy and metaphor often co-occur in natural language, yet computational work has studied them largely in isolation. We introduce a framework that transforms a literal sentence into three figurative variants: metonymic, metaphoric, and hybrid. Using this framework, we construct MetFuse, the first dedicated dataset of figurative fusion between metonymy and metaphor, containing 1,000 human-verified meaning-aligned quadruplets totaling 4,000 sentences. Extrinsic experiments on eight existing benchmarks show that augmenting training data with MetFuse consistently improves both metonymy and metaphor classification, with hybrid examples yielding the largest gains on metonymy tasks. Using this dataset, we also analyze how the presence of one figurative type impacts another. Our findings show that both human annotators and large language models better identify metonymy in hybrid sentences than in metonymy-only sentences, demonstrating that the presence of a metaphor makes a metonymic noun more explicit. Our dataset is publicly available at: https://github.com/cincynlp/MetFuse.

翻译：转喻和隐喻在自然语言中经常同时出现，但计算研究大多将它们孤立地研究。我们引入了一个框架，将字面句子转换为三种比喻变体：转喻式、隐喻式和混合式。利用该框架，我们构建了MetFuse——首个专门针对转喻与隐喻之间比喻融合的数据集，包含1,000个人工验证、语义对齐的四元组，总计4,000个句子。在八个现有基准上的外部实验表明，用MetFuse扩充训练数据能持续提升转喻和隐喻分类的性能，其中混合样本在转喻任务上取得了最大的增益。利用该数据集，我们还分析了一种比喻类型的存在如何影响另一种。我们的发现表明，无论是人工标注者还是大型语言模型，在混合句子中识别转喻的性能均优于仅在纯转喻句子中，这说明隐喻的存在使转喻名词更加显式。我们的数据集公开于：https://github.com/cincynlp/MetFuse。