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contextualized word representations

Contextualized word representations are numerical vectors used in natural language processing to encode the meaning of words dynamically based on the surrounding text in which they appear. Unlike traditional static word embeddings, which assign a single fixed vector to a word regardless of how it is used, contextualized representations generate distinct vector values that reflect a word's specific syntactic role, semantic nuances, and polysemy within a particular sentence or passage. Typically produced by deep neural network architectures such as bidirectional recurrent models or transformer-based language models, these dynamic vectors capture rich linguistic context, enabling computational systems to more accurately interpret ambiguous terms and perform a wide range of language understanding tasks.

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

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

Kawin Ethayarajh

OrganizationsStanford University

Why you should read this

Reveals the geometric behavior of BERT, ELMo, and GPT-2 embeddings across layers, proving that upper layers generate more context-specific representations while static word vectors account for less than five percent of their variance.

Replacing static word embeddings with contextualized word representations has yielded significant improvements on many NLP tasks. However, just how contextual are the contextualized representations produced by models such as ELMo and BERT? Are there infinitely many context-specific representations for each word, or are words essentially assigned one of a finite number of word-sense representations? For one, we find that the contextualized representations of all words are not isotropic in any layer of the contextualizing model. While representations of the same word in different contexts still have a greater cosine similarity than those of two different words, this self-similarity is much lower in upper layers. This suggests that upper layers of contextualizing models produce more context-specific representations, much like how upper layers of LSTMs produce more task-specific representations. In all layers of ELMo, BERT, and GPT-2, on average, less than 5% of the variance in a word's contextualized representations can be explained by a static embedding for that word, providing some justification for the success of contextualized representations.

Added

2026-09-25