We propose a metaphor detection architecture that is structured around two main modules: an expectation component that estimates representations of literal word expectations given a context, and a realization component that computes representations of actual word meanings in context. The overall architecture is trained to learn expectation-realization (ER) patterns that characterize metaphorical uses of words. When evaluated on three metaphor datasets for within distribution, out of distribution, and novel metaphor generalization, the proposed method is shown to obtain results that are competitive or better than state-of-the art. Further increases in metaphor detection accuracy are obtained through ensembling of ER models.

翻译：我们提出一种隐喻检测架构，该架构围绕两个核心模块构建：期望组件，用于在给定语境下估算字面词期望的表示；实现组件，用于计算语境中实际词义的表示。整体架构通过训练来学习表征词语隐喻用法的期望-实现（ER）模式。在三个隐喻数据集上评估分布内、分布外及新颖隐喻泛化性能时，所提方法获得的结果与最先进水平相当或更优。通过集成ER模型，可进一步提升隐喻检测准确率。