  Natural language inference (NLI) is among the most challenging tasks in
natural language understanding. Recent work on unsupervised pretraining that
leverages unsupervised signals such as language-model and sentence prediction
objectives has shown to be very effective on a wide range of NLP problems. It
would still be desirable to further understand how it helps NLI; e.g., if it
learns artifacts in data annotation or instead learn true inference knowledge.
In addition, external knowledge that does not exist in the limited amount of
NLI training data may be added to NLI models in two typical ways, e.g., from
human-created resources or an unsupervised pretraining paradigm. We runs
several experiments here to investigate whether they help NLI in the same way,
and if not,how?
