构建首个隐喻与转喻融合数据集，提升语言理解模型性能
MetFuse: Figurative Fusion between Metonymy and Metaphor
- 将字面句转为三类修辞变体：转喻、隐喻和混合型
- 4000句人工验证语料，混合例句使转喻识别准确率提升最显著
- 发现隐喻能增强转喻词的可识别性，适合修辞分析与NLP训练
隐喻与转喻常在自然语言中共同出现，但现有计算研究多将其孤立处理。本文提出一种框架，将字面句转换为三种修辞变体：转喻、隐喻和混合型。基于该框架，构建了首个专门针对转喻与隐喻融合的数据集MetFuse，包含1000组经人工验证的意义对齐四元组，共4000个句子。在8个现有基准上的外生实验表明，用MetFuse扩充训练数据能持续提升转喻与隐喻分类性能，其中混合样本在转喻任务上带来最大增益。此外，通过该数据集分析发现，无论是人类标注者还是大语言模型，在混合句子中识别转喻的表现优于仅含转喻的句子，说明隐喻的存在使转喻名词更显性。数据集已公开于：https://github.com/cincynlp/MetFuse。
原文摘要 · Abstract (English)
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.
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