Dispersing the Clouds of Doubt: Can Cosine Similarity of Word Embeddings Help Identify Relation-Level Metaphors in Slovene?

Word embeddings and pre-trained language models have achieved great performance in many tasks due to their ability to capture both syntactic and semantic information in their representations. The vector space representations have also been used to identify figurative language shifts such as metaphors, however, the more recent contextualized models have mostly been evaluated via their performance on downstream tasks. In this article, we evaluate static and contextualized word embeddings in terms of their representation and unsupervised identification of relation-level (ADJ-NOUN, NOUN-NOUN) metaphors in Slovene on a set of 24 literal and 24 metaphorical phrases. Our experiments show very promising results for both embedding methods, however, the performance in contextual embeddings notably depends on the layer involved and the input provided to the model.