  Pre-trained language models (PLMs) like BERT have made significant progress
in various downstream NLP tasks. However, by asking models to do cloze-style
tests, recent work finds that PLMs are short in acquiring knowledge from
unstructured text. To understand the internal behaviour of PLMs in retrieving
knowledge, we first define knowledge-baring (K-B) tokens and knowledge-free
(K-F) tokens for unstructured text and ask professional annotators to label
some samples manually. Then, we find that PLMs are more likely to give wrong
predictions on K-B tokens and attend less attention to those tokens inside the
self-attention module. Based on these observations, we develop two solutions to
help the model learn more knowledge from unstructured text in a fully
self-supervised manner. Experiments on knowledge-intensive tasks show the
effectiveness of the proposed methods. To our best knowledge, we are the first
to explore fully self-supervised learning of knowledge in continual
pre-training.
