 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 senseswith 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 .
<coherence>  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 senseswith 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 .
 Our results show that "tails" are the major cause of anisotropy of encoders' raw vector spaces, and clipping them leads to increased similarity across vectors. We demonstrate this in practice by showing that clipped vectors can more accurately distinguish word senses, as well as lead to better sentence embeddings when mean pooling. In three supervised tasks, we find that clipping does not affect the performance .