  Probing complex language models has recently revealed several insights into
linguistic and semantic patterns found in the learned representations. In this
paper, we probe BERT specifically to understand and measure the relational
knowledge it captures. We utilize knowledge base completion tasks to probe
every layer of pre-trained as well as fine-tuned BERT (ranking, question
answering, NER). Our findings show that knowledge is not just contained in
BERT's final layers. Intermediate layers contribute a significant amount
(17-60%) to the total knowledge found. Probing intermediate layers also reveals
how different types of knowledge emerge at varying rates. When BERT is
fine-tuned, relational knowledge is forgotten but the extent of forgetting is
impacted by the fine-tuning objective but not the size of the dataset. We found
that ranking models forget the least and retain more knowledge in their final
layer. We release our code on github to repeat the experiments.
