问题
I recently learned about the following phenomenon: Google BERT word embeddings of well-known state-of-the-art models seem to ignore the measure of semantical contrast between antonyms in terms of the natural distance(norm2 or cosine distance) between the corresponding embeddings. For example:
The measure is the "cosine distance" (as oppose to the "cosine similarity"), that means closer vectors are supposed to have smaller distance between them. As one can see, BERT states "weak" and "powerful" to be closer than "strong" and "powerfull". The same behavior reproduces with the norm2 distance:
Is anyone familiar with this behavior and how one can overcome it and detect a contrast between word senses?
来源：https://stackoverflow.com/questions/64738455/google-bert-and-antonym-detection