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Contextualized word embeddings, i.e. vector representations for words incontext, are naturally seen as an extension of previous noncontextualdistributional semantic models. In this work, we focus on BERT, a deep neuralnetwork that produces contextualized embeddings and has set thestate-of-the-art in several semantic tasks, and study the semantic coherence ofits embedding space. While showing a tendency towards coherence, BERT does notfully live up to the natural expectations for a semantic vector space. Inparticular, we find that the position of the sentence in which a word occurs,while having no meaning correlates, leaves a noticeable trace on the wordembeddings and disturbs similarity relationships.

What do you mean, BERT? Assessing BERT as a Distributional Semantics Model