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

BERTnesia: Investigating the capture and forgetting of knowledge in BERT