Human languages are full of metaphorical expressions. Metaphors help people
understand the world by connecting new concepts and domains to more familiar
ones. Large pre-trained language models (PLMs) are therefore assumed to encode
metaphorical knowledge useful for NLP systems. In this paper, we investigate
this hypothesis for PLMs, by probing metaphoricity information in their
encodings, and by measuring the cross-lingual and cross-dataset generalization
of this information. We present studies in multiple metaphor detection datasets
and in four languages (i.e., English, Spanish, Russian, and Farsi). Our
extensive experiments suggest that contextual representations in PLMs do encode
metaphorical knowledge, and mostly in their middle layers. The knowledge is
transferable between languages and datasets, especially when the annotation is
consistent across training and testing sets. Our findings give helpful insights
for both cognitive and NLP scientists.

该研究使用多语言数据集探索预训练语言模型中所编码的隐喻性信息，结果显示这些模型的中间层主要包含了隐喻性知识，并且该知识在多语言和多数据集中具有泛化性。