  Though pre-trained language models such as Bert and XLNet, have rapidly
advanced the state-of-the-art on many NLP tasks, they implicit semantics only
relying on surface information between words in corpus. Intuitively, background
knowledge influences the efficacy of understanding. Inspired by this common
sense, we focus on improving model pretraining by leveraging explicit
knowledge. Different from recent research that optimize pretraining model by
knowledge masking strategies, we propose a simple but general method to combine
explicit knowledge with pretraining. To be specific, we first match knowledge
facts from knowledge graph (KG) and then add a knowledge injunction layer to
transformer directly without changing its architecture. The present study seeks
to find the direct impact of explicit knowledge on transformer per-training. We
conduct experiments on various datasets for different downstream tasks. The
experimental results show that solely by adding external knowledge to
transformer can improve the learning performance on many NLP tasks.
