The meaning of polysemous words often varies in a highly productive yet predictable way. Generalizing the regularity between conventional senses to derive novel word meaning is crucial for automated processing of non-literal language uses such as figurative expressions. We introduce a novel task called systematic word meta-sense extension (SWORME) to test and improve language models' ability to extend word meaning to denote new semantic domains (also called meta-senses) that bear regular semantic relations with existing senses. We found that language models prefer incremental lexical semantic change toward conceptually similar meta-senses such as logical metonymy, and are much worse at predicting highly non-literal meaning extensions such as metaphors. We propose a novel analogy-based method of word meaning extension, and show that it effectively improves language model systematicity in making both gradual and radical types of meta-sense extension. We further demonstrate that learning systematic meta-sense extensions benefits language models on multiple benchmarks of figurative language understanding.

翻译：多义词的含义常常以高度能产且可预测的方式变化。概括常规义项之间的规律性以推导新词义，对于自动处理比喻性表达等非字面语言用法至关重要。我们提出了一项名为"系统性词汇元语义扩展"（SWORME）的新任务，旨在测试并提升语言模型将词义扩展至全新语义领域（亦称为元语义）的能力，这些新领域与现有义项存在规律性语义关联。研究发现，语言模型倾向于向逻辑转喻等概念相似的元语义进行渐进式词汇语义变化，而在预测隐喻等高度非字面义扩展时表现欠佳。我们提出了一种基于类比的新型词义扩展方法，并证明该方法能有效提升语言模型在渐进式与激进式元语义扩展中的系统性。进一步研究表明，学习系统性元语义扩展有助于语言模型在多个比喻性语言理解基准测试中提升表现。