Contextualized word embeddings, i.e. vector representations for words in context, are naturally seen as an extension of previous noncontextual distributional semantic models. In this work, we focus on BERT, a deep neural network that produces contextualized embeddings and has set the state-of-the-art in several semantic tasks, and study the semantic coherence of its embedding space. While showing a tendency towards coherence, BERT does not fully live up to the natural expectations for a semantic vector space. In particular, we find that the position of the sentence in which a word occurs, while having no meaning correlates, leaves a noticeable trace on the word embeddings and disturbs similarity relationships.
@article{arxiv.1911.05758,
title = {What do you mean, BERT? Assessing BERT as a Distributional Semantics Model},
author = {Timothee Mickus and Denis Paperno and Mathieu Constant and Kees van Deemter},
journal= {arXiv preprint arXiv:1911.05758},
year = {2020}
}