Recently, contextualized word embeddings outperform static word embeddings on many NLP tasks. However, we still don't know much about the mechanism inside these internal representationsproduced by BERT . Do they have any common patterns? What are the relations between word sense and context ? We find that nearly all the contextualized word vectors of BERT and RoBERTa have some common patterns . For BERT, the 557^{th} element is always the smallest. For RoBERTa, the 588^{th} element is always the largest  and the 77^{th} element is the smallest. We call them as "tails" of models. We  find that these "tails" are the major cause of anisotrpy of the vector space. After "cutting the tails", the same word's different vectors are more similar to each other. The internal representations also perform better on word-in-context (WiC) task. These suggest that " cutting the tails" can decrease the influence of context and better represent word sense .
Recently, contextualized word embeddings outperform static word embeddings on many NLP tasks. However, we still do not know much about the mechanism inside these representations . Do they have any common patterns? If so, where do these patterns come from ? We find that almost all the contextualized word vectors of BERT and RoBERTa have a common pattern . For BERT, the 557^{th} element is always the smallest. For RoBERTa, the 588^{th} element is always the largest , and the 77^{th} element is the smallest. We call them "tails" of models. We introduce a new neuron-level method to analyze where these "tails" come from. We find that these "tails" are closely related to the positional information. We also investigate what will happen if we "cutting the tails" (zero-out). Our results show that "tails" are the major cause of anisotropy of vector space. After "cutting the tails", a word's different vectors are more similar to each other. The internal representations have a better ability to distinguish a word's different senses with the word-in-context (WiC) dataset. The performance on the word sense disambiguation task is better for BERT and unchanged for RoBERTa. We can also better induce phrase grammar from the vector space. These suggest that " tails" are less related to the sense and syntax information in vectors. These findings provide insights into the inner workings of contextualized word vectors .
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