  Although pre-trained language models (PLMs) have achieved state-of-the-art
performance on various natural language processing (NLP) tasks, they are shown
to be lacking in knowledge when dealing with knowledge driven tasks. Despite
the many efforts made for injecting knowledge into PLMs, this problem remains
open. To address the challenge, we propose \textbf{DictBERT}, a novel approach
that enhances PLMs with dictionary knowledge which is easier to acquire than
knowledge graph (KG). During pre-training, we present two novel pre-training
tasks to inject dictionary knowledge into PLMs via contrastive learning:
\textit{dictionary entry prediction} and \textit{entry description
discrimination}. In fine-tuning, we use the pre-trained DictBERT as a plugin
knowledge base (KB) to retrieve implicit knowledge for identified entries in an
input sequence, and infuse the retrieved knowledge into the input to enhance
its representation via a novel extra-hop attention mechanism. We evaluate our
approach on a variety of knowledge driven and language understanding tasks,
including NER, relation extraction, CommonsenseQA, OpenBookQA and GLUE.
Experimental results demonstrate that our model can significantly improve
typical PLMs: it gains a substantial improvement of 0.5\%, 2.9\%, 9.0\%, 7.1\%
and 3.3\% on BERT-large respectively, and is also effective on RoBERTa-large.
