  Neural language representation models such as BERT, pre-trained on
large-scale unstructured corpora lack explicit grounding to real-world
commonsense knowledge and are often unable to remember facts required for
reasoning and inference. Natural Language Inference (NLI) is a challenging
reasoning task that relies on common human understanding of language and
real-world commonsense knowledge. We introduce a new model for NLI called
External Knowledge Enhanced BERT (ExBERT), to enrich the contextual
representation with real-world commonsense knowledge from external knowledge
sources and enhance BERT's language understanding and reasoning capabilities.
ExBERT takes full advantage of contextual word representations obtained from
BERT and employs them to retrieve relevant external knowledge from knowledge
graphs and to encode the retrieved external knowledge. Our model adaptively
incorporates the external knowledge context required for reasoning over the
inputs. Extensive experiments on the challenging SciTail and SNLI benchmarks
demonstrate the effectiveness of ExBERT: in comparison to the previous
state-of-the-art, we obtain an accuracy of 95.9% on SciTail and 91.5% on SNLI.
