  While recent research on natural language inference has considerably
benefited from large annotated datasets, the amount of inference-related
knowledge (including commonsense) provided in the annotated data is still
rather limited. There have been two lines of approaches that can be used to
further address the limitation: (1) unsupervised pretraining can leverage
knowledge in much larger unstructured text data; (2) structured (often
human-curated) knowledge has started to be considered in neural-network-based
models for NLI. An immediate question is whether these two approaches
complement each other, or how to develop models that can bring together their
advantages. In this paper, we propose models that leverage structured knowledge
in different components of pre-trained models. Our results show that the
proposed models perform better than previous BERT-based state-of-the-art
models. Although our models are proposed for NLI, they can be easily extended
to other sentence or sentence-pair classification problems.
