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Although models using contextual word embeddings have achievedstate-of-the-art results on a host of NLP tasks, little is known about exactlywhat information these embeddings encode about the context words that they areunderstood to reflect. To address this question, we introduce a suite ofprobing tasks that enable fine-grained testing of contextual embeddings forencoding of information about surrounding words. We apply these tasks toexamine the popular BERT, ELMo and GPT contextual encoders, and find that eachof our tested information types is indeed encoded as contextual informationacross tokens, often with near-perfect recoverability-but the encoders vary inwhich features they distribute to which tokens, how nuanced their distributionsare, and how robust the encoding of each feature is to distance. We discussimplications of these results for how different types of models breakdown andprioritize word-level context information when constructing token embeddings.

Spying on your neighbors: Fine-grained probing of contextual embeddings for information about surrounding words