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Replacing static word embeddings with contextualized word representations hasyielded significant improvements on many NLP tasks. However, just howcontextual are the contextualized representations produced by models such asELMo and BERT? Are there infinitely many context-specific representations foreach word, or are words essentially assigned one of a finite number ofword-sense representations? For one, we find that the contextualizedrepresentations of all words are not isotropic in any layer of thecontextualizing model. While representations of the same word in differentcontexts still have a greater cosine similarity than those of two differentwords, this self-similarity is much lower in upper layers. This suggests thatupper layers of contextualizing models produce more context-specificrepresentations, much like how upper layers of LSTMs produce more task-specificrepresentations. In all layers of ELMo, BERT, and GPT-2, on average, less than5% of the variance in a word's contextualized representations can be explainedby a static embedding for that word, providing some justification for thesuccess of contextualized representations.

How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings