  Several recent efforts have been devoted to enhancing pre-trained language
models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs
(KGs) and achieved consistent improvements on various knowledge-driven NLP
tasks. However, most of these knowledge-enhanced PLMs embed static sub-graphs
of KGs ("knowledge context"), regardless of that the knowledge required by PLMs
may change dynamically according to specific text ("textual context"). In this
paper, we propose a novel framework named Coke to dynamically select contextual
knowledge and embed knowledge context according to textual context for PLMs,
which can avoid the effect of redundant and ambiguous knowledge in KGs that
cannot match the input text. Our experimental results show that Coke
outperforms various baselines on typical knowledge-driven NLP tasks, indicating
the effectiveness of utilizing dynamic knowledge context for language
understanding. Besides the performance improvements, the dynamically selected
knowledge in Coke can describe the semantics of text-related knowledge in a
more interpretable form than the conventional PLMs. Our source code and
datasets will be available to provide more details for Coke.
