  Nature language inference (NLI) task is a predictive task of determining the
inference relationship of a pair of natural language sentences. With the
increasing popularity of NLI, many state-of-the-art predictive models have been
proposed with impressive performances. However, several works have noticed the
statistical irregularities in the collected NLI data set that may result in an
over-estimated performance of these models and proposed remedies. In this
paper, we further investigate the statistical irregularities, what we refer as
confounding factors, of the NLI data sets. With the belief that some NLI labels
should preserve under swapping operations, we propose a simple yet effective
way (swapping the two text fragments) of evaluating the NLI predictive models
that naturally mitigate the observed problems. Further, we continue to train
the predictive models with our swapping manner and propose to use the deviation
of the model's evaluation performances under different percentages of training
text fragments to be swapped to describe the robustness of a predictive model.
Our evaluation metrics leads to some interesting understandings of recent
published NLI methods. Finally, we also apply the swapping operation on NLI
models to see the effectiveness of this straightforward method in mitigating
the confounding factor problems in training generic sentence embeddings for
other NLP transfer tasks.
